simulation-based evaluation of disturbances of production

SIMULATION-BASED EVALUATION OF DISTURBANCES

OF PRODUCTION AND LOGISTIC PROCESSES IN

MECHANIZED TUNNELING OPERATIONS

SUBMITTED BY

TOBIAS RAHM

A DISSERTATION PRESENTED TO THE

DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING OF THE

RUHR-UNIVERSITÄT BOCHUM

IN CANDIDACY FOR THE DEGREE OF DOCTOR OF ENGINEERING

FIRST REVIEWER: PROF. DR.-ING. MARKUS KÖNIG

CHAIR OF COMPUTING IN ENGINEERING


SECOND REVIEWER: PROF. DR.-ING. MARKUS THEWES

INSTITUTE FOR TUNNELLING AND CONSTRUCTION MANAGEMENT


JUNE 2016

[I]

Abstract

This thesis presents an approach based on process simulation to quantify the influence of

disturbances on operational productivity in mechanized tunneling. The limitations of the

conventional planning methods are discussed in the thesis. These deficiencies include low

transparency of assumptions and generated results, difficulties in considering uncertainty,

abstraction of complex process interactions, and lack of detailed investigation of disturbances.

It is generally agreed that simulation is a suitable tool to address such deficiencies. The

developed approach is based on a formal model description with standardized and graphical

modeling notation. Three patterns of operational disturbances are distinguished in the thesis

and modeled with graphical modeling notation. These disturbance patterns are disturbances

directly affecting production, disturbances related to the supply chain, and cascading

disturbances. Cascading disturbances may arise if the duration of a disturbance exceeds a

certain threshold. This is exemplified in this thesis by the disposal of backfill grout to avoid

hardening in the system.

Based on the formal model, a modular simulation approach is developed in the general

purpose simulation framework. The elements identified in the system analysis are implemented

as distinct simulation components. This ensures reusability and flexibility and enables the

extension of the presented approach with additional simulation components to account for

further developments or new areas of application.

The operational data of a reference project is processed to suffice as input data of the

simulation study. This is done by structuring the data and application of the distribution fitting

method. Probability distribution functions are derived to express uncertainties in process

durations, as well the occurrence and duration of operational disturbances. The data from the

reference project is applied in a case study to demonstrate the influence of the three disturbance

patterns. Each disturbance pattern is presented in a separate example of application.

The presented approach holistically addresses the interdependencies between production

and logistic processes of mechanized tunneling influenced by uncertainties and disturbances.

This allows the identification of logistic bottlenecks. The explicit consideration of uncertainties

and detailed modeling of interdependencies provides robust results for project scheduling.

The modularized simulation approach reduces the effort needed for model development and

allows transparent evaluation of the alternatives. Decision-making is thus supported by a

transparent planning tool.

[II]

Contents

Abstract ................................................................................................................................................. I

Contents .............................................................................................................................................. II

List of figures ...................................................................................................................................... V

List of tables................................................................................................................................... VIII

List of abbreviations ...................................................................................................................... IX

1 Introduction ............................................................................................................................. 1

1.1 Mechanized tunneling ................................................................................................................................. 1

1.1.1 General process description of shielded tunneling .......................................................................... 3

1.1.2 Categorization of production and logistic processes ....................................................................... 4

1.2 Problem statement ....................................................................................................................................... 5

1.2.1 Reasons for downtime ................................................................................................................................... 6

1.2.2 Addressing downtime in project planning ........................................................................................ 10

1.2.3 Discussion of common planning practice .......................................................................................... 11

1.3 Thesis outline .............................................................................................................................................. 13

2 Motivation .............................................................................................................................. 15

2.1 Review of related research .................................................................................................................... 15

2.1.1 Simulation-based approaches for planning in the construction industry .......................... 15

2.1.2 General purpose simulation frameworks for construction operations................................ 17

2.1.3 Applications of process simulation in the tunneling industry.................................................. 22

2.1.4 Application of surrogate models for the estimation of TBM performance ......................... 26

2.1.5 Research addressing disturbances of construction operations ............................................... 28

2.2 Research motivation ................................................................................................................................. 31

2.3 Research goals ............................................................................................................................................. 35

2.4 Methodology ................................................................................................................................................ 35

3 Background ........................................................................................................................... 39

3.1 Simulation ..................................................................................................................................................... 39

3.1.1 General introduction ................................................................................................................................... 39

3.1.2 Procedure model for simulation studies ............................................................................................ 40

3.1.3 Simulation concepts .................................................................................................................................... 43

3.2 Conceptual model description .............................................................................................................. 46


3.2.1 System Modeling Language ...................................................................................................................... 47

3.3 Input data for simulation models ........................................................................................................ 50


3.3.2 Addressing uncertainty in input modeling ....................................................................................... 51

[III]

4 Formalization of the mechanized tunneling domain ............................................. 53

4.1 Specification of the system analysis scope ...................................................................................... 53

4.2 Structural decomposition ....................................................................................................................... 54

4.2.1 Tunnel boring machine .............................................................................................................................. 55

4.2.2 Backup system ............................................................................................................................................... 57

4.2.3 Tunnel ................................................................................................................................................................ 61

4.2.4 Surface facility ................................................................................................................................................ 62

4.3 Composition of three sample projects .............................................................................................. 65

4.4 Material-handling operations related to vehicle .......................................................................... 68

4.5 Specification of material flows ............................................................................................................. 70

4.5.1 Grout ................................................................................................................................................................... 70

4.5.2 Segmental lining ............................................................................................................................................ 71

4.5.3 Muck ................................................................................................................................................................... 72

4.6 Process specification of system elements ........................................................................................ 73

4.6.1 Cutting wheel .................................................................................................................................................. 74

4.6.1 Erector ............................................................................................................................................................... 75

4.6.2 Thrust cylinder .............................................................................................................................................. 75

4.6.3 Backfill grouting system ............................................................................................................................ 76

4.6.4 Mucking device on TBM ............................................................................................................................. 76

4.6.5 Segment-handling device of backup system..................................................................................... 78

4.6.6 Vehicle ............................................................................................................................................................... 80

4.6.7 Gantry crane.................................................................................................................................................... 82

4.7 Process interactions .................................................................................................................................. 83

4.7.1 Interaction of elements during advance............................................................................................. 83

4.7.1 Interaction of elements during ringbuild .......................................................................................... 84

5 Modeling disturbances ...................................................................................................... 85

5.1 Disturbances directly affecting production .................................................................................... 85

5.2 Disturbances related to the supply chain ........................................................................................ 87

5.3 Cascading disturbances ........................................................................................................................... 88

6 Operational input data for the developed simulation approach ........................ 91

6.1 Acquisition of project data ..................................................................................................................... 91

6.2 Introduction to reference project........................................................................................................ 92

6.3 Deriving input data ................................................................................................................................... 93

6.3.1 Distribution fitting with ExpertFit ........................................................................................................ 94

6.3.2 Production-related input data ................................................................................................................ 96

6.3.3 Disturbance-related input data .............................................................................................................. 96

7 Implementation in a simulation framework ............................................................. 98

7.1 AnyLogic simulation framework ......................................................................................................... 98

[IV]

7.2 Random sampling in AnyLogic ............................................................................................................. 99

7.3 Structure of simulation modules ...................................................................................................... 100

7.4 Behavior of simulation modules ....................................................................................................... 103

7.4.1 Implementation of processes ............................................................................................................... 103

7.4.2 Implementation of material flow ........................................................................................................ 104

7.5 Process interactions between simulation modules .................................................................. 107

7.6 Implementation of disturbances ...................................................................................................... 108

8 Case Study ............................................................................................................................ 110

8.1 Demonstration of the developed approach by application examples ............................... 110

8.1.1 Application example 1 ............................................................................................................................. 110



8.2 Discussion of the simulation study .................................................................................................. 115

8.2.1 Comparison of the application examples ........................................................................................ 115

8.2.2 Comparison of simulation results with project data ................................................................. 117

9 Final remarks ..................................................................................................................... 119

9.1 Summary .................................................................................................................................................... 119

9.2 Conclusion .................................................................................................................................................. 122

9.3 Outlook ........................................................................................................................................................ 124

List of references ............................................................................................................................ IX

Appendix A – SysML block definition diagrams ............................................................. XXIII

Appendix B – SysML internal block definition diagrams .............................................. XXV

Appendix C – SysML state machine diagrams .................................................................. XXVI

Appendix D – SysML sequence diagrams ....................................................................... XXVIII

Appendix E – Distribution fitting results........................................................................... XXXI

Acknowledgement .................................................................................................................. XXXIX

Curriculum vitae ............................................................................................................................ XL

[V]

List of figures

Figure 1: The four main process chains in mechanized tunneling ................................................................ 5

Figure 2: CYCLONE elements based on Halpin and Riggs [1992] ............................................................... 18

Figure 3: Methodical concept of the presented approach .............................................................................. 36

Figure 4: Methods of system investigation according to Law [2014] ....................................................... 40

Figure 5: Procedure model for simulation studies according to Banks et al. [2001] ......................... 41

Figure 6: Procedure model for simulation studies according to Rabe et al. [2008b] ......................... 42

Figure 7: Representation of event occurrence over time according to Law [2014] ........................... 44

Figure 8: Simplified system dynamic model of the world population ...................................................... 46

Figure 9: SysML taxonomy according to OMG [2015a] ................................................................................... 49

Figure 10: Context diagram of the mechanized tunneling domain ............................................................ 54

Figure 11: SysML bdd showing the general components of TBM ............................................................... 55

Figure 12: SysML bdd showing the basic functionalities of backup system ........................................... 58

Figure 13: SysML bdd showing possible mucking devices of the backup system ................................ 58

Figure 14: SysML bdd showing possible grout-handling devices of the backup system .................. 59

Figure 15: SysML bdd showing possible segment-handling devices of the backup system ............ 60

Figure 16: SysML bdd for the tunnel domain ...................................................................................................... 62

Figure 17: SysML bdd for the surface facility domain ..................................................................................... 64

Figure 18: SysML bdd showing the decomposition of a slurry shield machine setup ....................... 65

Figure 19: SysML bdd showing the decomposition of cart-based mucking for EPB shields ........... 66

Figure 20: SysML bdd showing the decomposition of belt-based mucking for EPB shields ........... 67

Figure 21: SysML ibd showing the vehicle’s relations to material-handling operations .................. 68

Figure 22: SysML ibd showing materials transported by vehicle ............................................................... 69

Figure 23: SysML ibd the showing material flow for one-component grouting ................................... 70

Figure 24: SysML ibd showing the segment transport for a setup with a segment feeder .............. 71

Figure 25: SysML ibd showing mucking in Slurry shields ............................................................................. 72

Figure 26: SysML ibd showing belt-based mucking in EPB shields ........................................................... 73

Figure 27: SysML ibd showing cart-based mucking in EPB shields ........................................................... 73

Figure 28: SysML stm of cutting wheel .................................................................................................................. 74

Figure 29: SysML stm of erector ............................................................................................................................... 75

Figure 30: SysML stm of thrust cylinder ............................................................................................................... 76

Figure 31: SysML stm of backfill grouting system ............................................................................................. 76

Figure 32: SysML stm of screw conveyor.............................................................................................................. 77

Figure 33: SysML stm of slurry pump .................................................................................................................... 78

[VI]

Figure 34: SysML stm of segment crane ................................................................................................................ 79

Figure 35: SysML stm of segment feeder .............................................................................................................. 80

Figure 36: SysML stm of vehicle ............................................................................................................................... 81

Figure 37: SysML stm of gantry crane .................................................................................................................... 82

Figure 38: SysML sd showing the interaction during advance with slurry shields ............................. 83

Figure 39: SysML sd showing the interaction during ringbuild .................................................................. 84

Figure 40: Enhanced SysML stm of the backfill grouting system to consider disturbances ............ 86

Figure 41: Enhanced SysML stm of erector to consider disturbances during ringbuild ................... 87

Figure 42: Qualitative visualization of a cascading disturbance during advance ................................ 89

Figure 43: SysML sd showing cascading disturbances affecting ringbuild ............................................. 90

Figure 44: Unscaled representation of reference project as cross-section ............................................. 93

Figure 45: Time allocation of production processes and downtime of the reference project ........ 93

Figure 46: Graphical comparison of distribution fitting with density histogram plot ....................... 95

Figure 47: Graphical comparison of distribution fitting with frequency-comparison plot ............. 96

Figure 48: Screenshot of developed simulation components and the hierarchical structure ..... 101

Figure 49: Screenshot of interior setup of TBM to model an EPB shield .............................................. 101

Figure 50: Screenshot showing all simulation components applicable for backup system ......... 102

Figure 51: Screenshot showing all simulation components for surface jobsite facilities .............. 103

Figure 52: Screenshot showing the internal setup of the simulation component erector ............ 104

Figure 53: Screenshot showing the implementation of the multi-method simulation concept .. 105

Figure 54: Comparison of plain DES and the developed multi-method approach ........................... 106

Figure 55: Conceptual visualization of the event manager for flexible process interaction ........ 107

Figure 56: Screenshot of AnyLogic state machine to explicitly address disturbances ................... 108

Figure 57: Conceptual visualization of disturbance suspension for process inactivity.................. 109

Figure 58: Total makespan of 1000 simulation runs gathered with application example 1 ........ 112

Figure 59: Distribution of average durations in application example 1 ............................................... 112

Figure 60: Distribution of average durations in application example 2 ............................................... 114

Figure 61 Distribution of average durations in application example 3 ................................................. 115

Figure 62: Comparison of mean results generated in the three application examples .................. 116

Figure 63: Validation of the simulation approach with gathered project data .................................. 118

Figure 64: Basic steps of the procedure model followed in the presented thesis ............................ 119

Figure 65: SysML bdd for mucking device in tunnel domain .................................................................. XXIII

Figure 66: SysML bdd to show the generalization of cargo items ......................................................... XXIII

Figure 67: SysML bdd to show the generalization of relevant liquids ................................................. XXIV

Figure 68: SysML bdd to show the generalization of storages in the surface facility domain ... XXIV

Figure 69: SysML ibd of material flow in 2-component grouting ............................................................ XXV

[VII]

Figure 70: SysML ibd of segment transport for a setup with a segment transfer table.................. XXV

Figure 71: SysML stm for backup belt conveyor ........................................................................................... XXVI

Figure 72: SysML stm for tunnel belt conveyor ............................................................................................ XXVI

Figure 73: SysML stm for grout pump ............................................................................................................... XXVI

Figure 74: SysML stm for grout circuit – component A ............................................................................. XXVII

Figure 75: SysML sd showing the interaction during advance with EPB shield machines ...... XXVIII

Figure 76: SysML sd showing the interaction during material handling of grout ....................... XXVIII

Figure 77: SysML sd showing the interaction of segment handling with a segment feeder ....... XXIX

Figure 78: SysML sd showing the interaction during material handling at jobsite facility ......... XXIX

Figure 79: SysML sd showing the interaction during material of spoil in cart-based mucking .. XXX

Figure 80: Density-histogram plot for advance rate ................................................................................... XXXI

Figure 81: Density-histogram plot for segment installation .................................................................... XXXI

Figure 82: Density-histogram plot for pipe extension .............................................................................. XXXII

Figure 83: Density-histogram plot for TBF of erector ............................................................................. XXXIII

Figure 84: Density-histogram plot for TTR of erector ............................................................................. XXXIII

Figure 85: Density-histogram plot TBF of cutting wheel ........................................................................ XXXIV

Figure 86: Density-histogram plot TTR of cutting whee ......................................................................... XXXIV

Figure 87: Density-histogram plot TBF of slurry circuit .......................................................................... XXXV

Figure 88: Density-histogram plot for TTR of slurry circuit ................................................................... XXXV

Figure 89: Density-histogram plot for TBF of backfill grouting unit .................................................. XXXVI

Figure 90: Density-histogram plot for TTR of backfill grouting unit .................................................. XXXVI

Figure 91: Density-histogram for TBF of thrust cylinders .................................................................... XXXVII

Figure 92: Density-histogram plot for TTR of thrust cylinders .......................................................... XXXVII

Figure 93: Density-histogram plot for TBF of vehicle .......................................................................... XXXVIII

Figure 94: Density-histogram plot for TTR of vehicle .......................................................................... XXXVIII

[VIII]

List of tables

Table 1: Comparison of simulation-based approaches concerning relevant aspects ........................ 33

Table 2: Exemplary export of work shift report in tabular format ............................................................ 91

Table 3: Probability distribution functions to model production and logistic processes ................. 96

Table 4: Parameters of probability distribution functions for TBF of simulation components

stringently required for execution of production processes ..................................................................... 111

Table 5: Parameters of probability distribution functions for TTR of simulation components

stringently required for execution of production processes ..................................................................... 111

Table 6: Parameters of probability distribution functions for TBF of simulation components of

the supply chain ............................................................................................................................................................ 113

Table 7: Parameters of probability distribution functions of TTR of simulation components of the

supply chain ................................................................................................................................................................... 113

Table 8: Comparison of simulation results........................................................................................................ 117

[IX]

List of abbreviations

Abbreviation Full name

ACT Activity Diagram

ANN Artificial Neuronal Networks

BIM Building Information Modeling

BDD Block Definition Diagram

BPMN Business Process Modeling Notation

BS Backup System

CAE Computer-Aided Engineering

CIPROS Construction Integrated Project and Process Planning Simulation

COOPS Construction Object-Oriented Process Simulation

COSYE Construction Synthetic Environment

CPM Critical Path Method

CSM Colorado School of Mines

CYCLONE CYClic Operations Network

DAT Decision Aids for Tunneling

DBN Dynamic Bayesian Networks

DES Discrete Event Simulation

DISCO Dynamic Interface for the Simulation of Construction Operations

ENV Environment

EPB Earth Pressure Balanced

HLA High Level Architecture

HMM Hidden Markov Modeling

IBD Internal Block Definition Diagram

IDEF Integrated Definition

IEEE Institute of Electrical and Electronics Engineers

LEP Lower End Point

MFEMA Machinery Failure Mode and Effects Analysis

MIT Massachusetts Institute of Technology

MSV Multi-service vehicle

NTNU Norwegian University of Science and Technology

OCL Object Constraint Language

OMG Object Management Group

OPT Optional Frame

PAR Parametric Diagram

[X]

PDL Process Description Language

PERT Program Evaluation and Review Technique

PKG Package Diagram

PLC Programmable Logic Controller

PSO Particle Swarm Optimization

REQ Requirement Diagram

SD Sequence Diagram

SDESA Simplified Discrete Event Simulation Approach

SOM Self-Organizing Maps

SPS Special Purpose Simulation

SSCS SIMPHONY Supply Chain Simulator

STM State Machine

STROBOSCOPE STate and ResOurce Based Simulation of Construction ProcEsses

SysML System Modeling Language

TBF Time Between Failures

TBM Tunnel Boring Machine

TTR Time To Recover

UC Use Case Diagram

UEP Upper End Point

UML Unified Modeling Language

V&V Verification and Validation

VBA Visual Basic for Applications

[1]

1 Introduction

Urbanization is a phenomenon that globally challenges development of cities. The tables

turned in 2007 when the urban population exceeded the number of people living in rural areas

for the first time. This trend has continued, and 54% of the world’s population lived in cities and

associated suburbs in 2014 [United Nations, 2014]. While the number of mega-cities is rising,

smaller cities - with populations of 500,000 to one million - are also growing very fast [United

Nations, 2014]. Traffic congestion is frequently observed in fast-growing cities. City centers

usually have very restricted space. A suitable solution to adjust infrastructure to the demands of

urbanization is often found in underground construction.

As an alternative, Just and Thater [2008] highlight the extensive and very successful bus

system of Sao Paolo as a low-cost solution for commuting issues in wide-spread areas. However,

the success of Sao Paolo’s bus system is due to the existence of distinct bus lanes, separated from

private traffic. The recent extension of Sao Paolo’s metro system indicates that the advantages of

underground infrastructure are predominant [Rocha et al., 2014]. It is expected that there will

be a huge demand for the construction of metro lines in underdeveloped cities around the world

[Thewes, 2014]. Besides the intraurban infrastructure, the connection between such

metropolitan areas also becomes more important in our consolidating world. In order to

minimize travel time, the alignment of high-speed railways or motorways often crosses bodies of

water or mountain ranges, which can also be addressed by tunnel construction.

Apart from traffic tunnels, the subterranean municipal grid of supply and disposal pipelines is

a great challenge for fast-growing cities and their associated areas. FIG [2010] highlights the

absurd situation of fresh water scarcity and waste water disposal problems existing

simultaneously in underdeveloped major and mega-cities. The above-ground disposal of

untreated waste water poses great risks for human health and environmental pollution. Mexico

City recently addressed this problem in a mega-project with the application of six tunnel boring

machines (TBMs) [Walis, 2013].

In conclusion, it can be stated that mining of underground infrastructure has increased in the

recent years [Maidl et al., 2012] and that this trend is expected to continue [Thewes, 2014].

1.1 Mechanized tunneling

Tunnel construction can be done by open or closed methods. Open methods require

excavation from the surface down to the planned tunnel invert and are thus also referred to as

surface methods. The tunnel can then be built openly and the trench is backfilled afterwards.

Open methods thus involve building a temporary trench along the complete alignment. In urban

areas, the existing constructions above the planned alignment often preclude open methods.

Even if the excavation is generally possible, the high degree of interference with daily traffic is a

major downside of open methods.

[2]

Closed methods on the other hand involve minimal interference and allow tunneling below

existing development. Closed tunneling methods can be categorized as conventional tunneling

or mechanized tunneling. Conventional tunneling involves application of explosives or

mechanical excavators. These methods allow variations in the size and form of tunnel cross-

sections, but require a certain inherent stability of the soil for the installation of support

measures [Maidl et al., 2012].

The application of TBMs for construction is generally referred to as mechanized tunneling.

Mechanized tunneling has several advantages, including fast and safe mining, a high degree of

automatization and prefabrication, precise drilling even at shallow depths, minimal and

controlled impact on surface development, and control of groundwater [Maidl et al., 2012].

However, there are some disadvantages. The very high initial costs of the TBM itself and its

assembly are usually only economical for tunnels longer than 1000 m.

TBM technology has seen tremendous developments over the past decades and now allows

mining under extreme conditions, e.g. withstanding 15 bars of water pressure [Herrenknecht

AG, 2014], or in impressive dimensions [Sleight, 2015; Herrenknecht AG, 2010].

The German Tunneling Committee provides a holistic classification of TBMs [DAUB, 2010].

The type of machine applied mostly depends on the soil conditions encountered [Maidl et al.,

2012]. TBMs are generally distinguished for hard rock and soft rock. A further distinction is

made between TBMs with and without a shield, to prevent cave-ins in unstable ground

conditions. While hard rock TBMs might also have a shield, the TBMs applied in unstable soft

grounds are generally referred to as shielded machines. In order to provide face stability and

minimize surface settlements in unstable geotechnical conditions, different kinds of face-support

mechanisms have been developed and are further classified [DAUB, 2010]. Active face-support

provides the possibility to counteract the forces of earth and water pressure acting at the tunnel

face. This thesis is focused on two types of shielded TBMs most commonly used nowadays: the

Earth Pressure Balanced (EPB) shield machine and the Slurry shield machine. EPB shields

process the excavated soil into a pastry muck that can be pressurized. Slurry shields on the other

hand mix the excavated material with a bentonite suspension. The bentonite suspension slurries

the soil and allows a very precise pressure control [Maidl et al., 2012].

The decision for selecting a face-supporting system depends on the prevailing geotechnical

conditions. EPB shields are mainly applied in cohesive soils with good plastic properties. Early

EPB shield machines could only be used in fine-grained soils with at least 20–30% silt and clay

that would enable the soil to be processed into an adequate support medium [Maidl et al., 2012].

However, the process technology of EPB shield machines has been constantly improved and

EPBs are nowadays successfully deployed in various soil conditions [Herrenknecht et al., 2011].

Their application in non-cohesive grounds requires the modification of soil properties by

conditioning, in order to provide suitable characteristics for use as a support medium [Thewes

and Budach, 2010]. Nine out of 10 machines used in situations where an active face-support is

required are EPB shield machines [Herrenknecht et al., 2011].

[3]

1.1.1 General process description of shielded tunneling

As stated above, the thesis focuses on EPB and slurry shields. The following description is

thus limited to these two machine types [Maidl et al., 2012].

Tunneling in unstable ground conditions requires that the excavated space must be secured.

In shielded tunneling, this is usually done by installing a ring of reinforced concrete segments

inside the shield. The installation of support measures is thus referred to as ringbuild.

Thrust cylinders in the TBM use the last segmental ring as an abutment to push the machine

into the ground. Tools attached to the rotating cutting wheel loosen the ground, which enters

through openings into the excavation chamber. Soil with certain characteristics tends to clog

these openings or other downstream elements [Hollmann and Thewes, 2012]. Clogging is a

serious risk in tunneling operations as it can result in significant performance loss and should be

addressed if possible [Thewes and Burger, 2005].

The excavation chamber is the place where the active face-support is essentially established

and controlled. Both EPB and slurry shields provide a sophisticated set of sensors to monitor the

pressure of the support medium. A bulkhead separates the pressurized excavation chamber

from the atmospheric conditions in the tunnel. Slurry shields inject a bentonite suspension into

the excavation chamber. It forms a filter cake on the tunnel face that transfers the support

pressure onto the ground. In EPB shields, it could be necessary to apply conditioning agents to

process fine-grained soil for the desired plastic support medium [Thewes and Budach, 2010].

The conditioning can be applied both inside the excavation chamber and directly at the tunnel

face.

The extraction of muck from the excavation chamber is done differently in EPB and slurry

shield machines. Slurry shields feature a closed pipeline circuit between the TBM’s excavation

chamber and the surface jobsite. The feeding line of this slurry circuit pumps a bentonite

suspension at the tunnel face, where it absorbs the loosened soil. This slurry suspension is then

extracted from the excavation chamber and pumped to the surface. Due to economic reasons,

the slurry suspension is processed in a soil treatment plant to re-separate soil and bentonite,

allowing the latter to be reused to a certain extent. The extracted soil is stored in muck pits until

its removal from the jobsite by trucks. In EPB shield machines on the other hand, the excavated

earth is removed from the excavation chamber by a screw conveyor. Solid materials may need to

be broken down to prevent blockages. EPB shields can operate in open or closed mode. In this

context, closed mode involves active face support by a pressurized excavation chamber. In order

to maintain this pressure throughout mucking, the pressure must be dissipated along the screw

conveyor [Herrenknecht et al., 2011]. The soil is usually discharged onto a short belt conveyor as

part of the TBM’s backup trailer. After this, there are two distinct possibilities. The commonly

applied mucking method for EPB shields is still transport by tunnel trains. The soil is discharged

into muck carts that are then pulled to the surface or to the tunnel’s starting shaft. The carts are

then emptied into a muck pit by a crane. The other mucking method is a tunnel belt conveyor.

[4]

The belt is attached to the tunnel wall and enables continuous mucking as it directly leads to the

pit.

The shield generates an annular gap between the lining and the soil. During boring, this

annular gap is constantly backfilled with a quick-hardening grout. Grouting is a crucial process

to avoid surface settlements and provide a sound bedding of the lining segments in the

surrounding ground [Thewes and Budach, 2008].

The boring process ends after excavation of a certain length, more or less equal to the

average length of one segmental ring. The machines usually move only during excavation and

not during ringbuild. Therefore, the excavation process is also referred to as advance. When

advance is complete, the installation of lining can start. The lining segments are prefabricated

and stored in sufficient numbers at the jobsite to ensure undisturbed progress. For each advance

step, all the segments of a full ring need to be transported from the surface jobsite to the TBM.

This is done by either the aforementioned trains or tire-based multi-service vehicles (MSV). The

term vehicle is used as a generalization hereinafter. A special segment crane in the backup

trailer unloads the segments from the vehicle’s trailer. Most TBMs feature a segment feeder that

can store all segments of the ring at once, thus significantly reducing the waiting period of the

delivering vehicle. Otherwise, the segments are discharged one at a time on a segment transfer

table, a process that retains the vehicle much longer.

The reinforced concrete segments can weigh more than a ton and usually require a robotic

assembly device to be put in place. This erector allows the precise positioning of the heavy

elements, while allowing six degrees of freedom. One segment is picked at a time from either a

feeder or a transfer table and placed in position. The segments have individual shapes that

enable changes in track by rotation of the ring. The required rotation is usually calculated via

software in relation to the current position and planned alignment. In order to place the

segments, the thrust cylinders in close proximity need to be retracted first. The segments are

then placed and the thrust cylinders extracted again to secure the segments. The final element in

the ringbuilding process is the wedge-shaped key stone to lock the segmental lining.

The end of the ringbuilding process also marks the end of a complete advance cycle.

Therefore, an advance cycle consists of advance (excavation) and ringbuild. If no auxiliary work

is needed, the next advance cycle can start with another excavation process.

1.1.2 Categorization of production and logistic processes

In the previous section, processes relevant to construction performance are described

generally. Taking a process-oriented view, four main process chains can be distinguished for

shielded tunneling: ground excavation, installation of support measures, supply of required

material, and disposal of excavated material.

Ground excavation (advance) and ringbuild are the production processes of mechanized

tunneling. They are performed by the TBM and define the general performance of a project. In

[5]

mechanized tunneling, the production processes are strictly sequential and cannot be performed

in a parallel manner. Each must be finished completely before the next process can start.

This sequential alternation is also a main characteristic of tunneling with shielded TBMs.

However, there are machines that allow a parallel execution of production processes. As there

are only very few examples, the simultaneous execution of production processes is not covered

in this thesis.

All the processes related to the supply and disposal of materials are summed up as logistic

processes as part of the supply chain. Supply chain processes can be executed parallel to core

processes. Depending on the structure and capacity, several logistic processes can also be

executed simultaneously within the supply chain.

However, interdependencies exist between production and logistic processes. Excavation, for

example, requires some kind of mucking operation. Moreover, the advance of the machine is

usually stopped if (quality) grouting of the annular gap is not provided. Ringbuild is suspended if

the segmental lining elements are not delivered timely. This brief description exemplifies the

interdependencies between production and logistic processes in mechanized tunneling.

Figure 1 shows the four process chains of mechanized tunneling and their relationships, as

described previously. Production is characterized by the sequential process execution of

advance and ringbuild. The two production processes are together referred to as the advance

cycle. The logistic process chains include disposal of excavated material (mucking) and supply of

material required for production. As shown in Figure 1, mucking starts and ends with advance.

The figure also depicts a standstill due to the missing material. The supply of segmental lining is

delayed in advance cycle 2 and ringbuild is suspended accordingly. Obviously, the figure depicts

only the underground delivery and not all the processes of the whole supply chain.

Figure 1: The four main process chains in mechanized tunneling

1.2 Problem statement

In practice, mechanized tunneling projects often involve a large amount of unproductive

standstill time. This thesis addresses this issue. The reasons for these unproductive periods are

discussed here. At this point, however, it should be stated that not every standstill can be

Advance Cycle 1 Time

Ringbuild

Advance

Ringbuild

Advance

Mucking

Supply

Mucking

Supply

Advance Cycle 2

Pro

du

ctio

n

Lo

gis

tic

Standstill

Advance

Processes

[6]

regarded as underperforming downtime. Technological dependencies often require a

production shut-down. Nevertheless, observations show that mechanized tunneling projects

often face performance losses that could have been addressed prior to project implementation.

This is a well-known fact among tunnel practitioners, even though there are almost no

publications acknowledging or even investigating this issue. Negative information is seldom

shared openly. Furthermore, tunneling projects usually have a complex structure of

stakeholders. Unsatisfactory performance is often associated with unforeseen costs and /or

increased project time. Consequently, reports discussing such projects and reasons for their

underperformance are usually restricted. Performance investigations in case studies are

generally done on quite successful projects and are thus not relevant for the topic discussed at

this point.

Most publications analyze the ratio of production to downtime focus for hard rock TBMs and

usually only address the drilling performance [Hassanpour et al., 2010].

Delisio et al. [2013] present the relative durations of downtime along with their reasons

while drilling the east tube of the Lötschberg Base Tunnel with a Gripper TBM. Unfortunately,

the sample set of data only accounts for 18 days and not for the whole project duration.

Frough and Rostami [2014] present the case study of an Iranian project where a double-

shield TBM was applied. The results show that the average utilization factor, defined as boring

time over total project time, is almost 20%. The authors also list several reasons for downtime,

including ringbuild with a double-shield TBM.

Maidl and Wingmann [2009] investigate the durations of advance, ringbuild, and standstill of

projects with EPB shield machines. The relevant influences on performance are discussed and

high-level process dependencies are described. A case study of a 9 km long tunnel mined by an

EPB shield 11 m in diameter is evaluated and the various reasons for standstills are presented in

terms of minutes per ring. A discussion concerning the ratio of productive to unproductive time

is not given.

Zhao et al. [2007] investigate shielded tunneling in mixed and especially varying ground

conditions in two projects in Singapore. Both tunnels were driven by EPB shields. The insights

gained from analyzing the southern drive were used to increase performance in the second

tunnel. These improvements solely concerned the boring process and especially the wear and

tear of cutting tools.

In summary, it can be stated that the problem of high standstill times in shielded tunneling is

barely addressed. The issue is well known among tunneling practitioners but the approaches to

transparently address it are missing. In the following section, a general discussion of standstill

reasons in shielded tunneling is given in order to identify potential areas for improvements.

1.2.1 Reasons for downtime

Depending on the point of view and the level of detail, a wide range of reasons for downtime

can be distinguished. The presented approach deals with utilization during the actual tunneling

[7]

phase. For this reason, the discussion does not cover installation of surface facilities, TBM

assembly, or launching phase. The actual production ends with the breakthrough. Consequently,

TBM disassembly and tunnel outfitting are also not considered here.

1. Difficult geotechnical conditions

2. Extension of supply and disposal facilities

3. Maintenance works

4. Technical failures

5. Insufficient or sensitive logistic processes

6. Modification of machine and logistic setup

7. Extraordinary events

In the following section, these categories are discussed individually.

1. Difficult geotechnical conditions

Geotechnical surveys are expensive and can be done on only a limited area along the tunnel

alignment. The geotechnical profile might be very different even a few meters outside the

exploratory hole. Sudden changes can be caused by fault zones, boulders, or cavities. Thus, the

geotechnical profile has uncertainties that might affect the tunnel construction negatively. If

typical counter-measurements such as jet grouting, artificial ground-freezing, or drawdowns are

impossible or do not achieve the desired effects, profound modifications must be made on the

machine and/or the logistic plan.

A tight exploration pattern is desirable, but it is very costly and has limited value when no

changes are revealed. What is more, dense construction in urban areas might render accurate

exploratory drillings along the tunnel alignment impossible. Available and accessible ground

investigations of previous construction projects are a valuable source of information to this

issue. However, downtimes originating due to this category are difficult to address in advance

and ground investigations remain uncertain to an extent.

2. Extension of supply and disposal facilities

The pipes and cables running back and forth between the TBM and the surface facility must

be extended regularly. These tasks are recurrent and foreseeable and thus often referred to as

planned downtimes. The extension of pipelines for fresh water, waste water, grout, air, and

slurry is usually done during ringbuild and thus does not require a production shut-down.

Downtime from these operations only occurs when the extension takes longer than the

ringbuild. If, for example, the extension of slurry pipes takes longer than ringbuild, the next

advance stroke must be suspended until the extension is finished.

The extension of electricity on the other hand is not possible without a production shut-

down, since other work in the tunnel or shaft area cannot be continued due to safety reasons. If

an EPB shield with tunnel belt is applied, this belt must also be extended regularly, typically

resulting in a full day without production. However, maintenance and cleaning work can be done

[8]

during belt extension. If trains are used for transportation, a regular extension of tracks is

required. This process can be done parallel to ringbuild. However, an interruption will occur if

track extension is not finished in time. Regardless of track or wheel-based tunnel logistic,

installation of passages is advised if the travel time exceeds the production cycle [Bruland,

1998]. The installation of such passages might cause additional disruptions. Since these tasks

follow a certain pattern (e.g. every 200 m), they are foreseeable and can be addressed by

planning.

3. Maintenance works

Basically, maintenance regimes can be distinguished into two categories: reactive and

proactive. Reactive maintenance is only performed when a part breaks or a failure occurs. This

regime does not require any regular interruptions of production. However, the functionality of

the entire machine decreases eventually and major disturbances might arise. A proactive

regiment on the other hand schedules regular maintenance works that can also be considered as

planned downtime.

The most prominent example in mechanized tunneling is the exchange of cutting tools. The

abrasiveness of the encountered soil highly influences the life cycle of the tools. If regular

exchanges are neglected, the drilling performance will decrease eventually and the cutting wheel

may be damaged.

Regular cleaning and flushing also contribute to higher performances. Examples of frequent

downtime due to neglected cleaning are related to the annular gap grouting and the extension of

slurry pipes.

In order to keep the influence of a proactive maintenance regime to a minimum, it should be

aligned with other foreseeable (planned) production standstills.

4. Technical failures

The production processes, advance and ringbuild, require the coordinated functioning of

several main elements. For ringbuild, the prefabricated lining segments must be lifted to a

specific position of installation. In order for this to happen, the erector must move the heavy

segments. Additionally, the thrust cylinders must be operable to temporarily secure the

segments until the ring is completely assembled. If either of these main elements does not

function properly, the execution of ringbuild is not possible.

Advance, on the other hand, depends on functioning thrust cylinders to push the machine

forward. Additionally, the drives of the cutting wheel must be operable to provide the rotation

for excavation. A muck hauling device, required for handling the excavated material, has to be

selected depending on the type of machine applied. In slurry shields, a slurry circuit is used for

mucking. The soil treatment plant connected to the slurry circuit is required for processing the

bentonite suspension. If EPBs are applied, a screw conveyor is used to extract the muck from the

excavation chamber. The muck can be transported through the tunnel via tunnel belts or

[9]

vehicles. For soft ground, grouting is mandatory during advance to ensure proper bedding of the

lining.

This short description shows that a failure might immediately interrupt the production,

including not only the functionality of the elements, but also the related electric and hydraulic

system.

Such failures are not predictable. However, a stochastic analysis of the gathered project data

can be done. While technical failures cannot be remedied, the results of such an analysis can be

considered in project planning.

5. Insufficient or sensitive logistic processes

Contrary to residential or industrial construction, tunneling has a homogenous and repetitive

supply chain. The required materials usually remain unchanged throughout project execution.

This is one reason that the influence of logistic processes is regularly underrated by project

managers. But the highly restricted available space and the long distances between shaft and

TBM hinder the timely supply of materials or capacities, which is essential for high

performances. Obviously, the longer is the tunnel, the more likely is interference due to under-

dimensioned supply chain.

As stated above, logistic and production processes can take place simultaneously.

Consequently, disturbances in logistic processes do not necessarily result in an immediate

production shut-down. A disturbance in segment delivery, for example, does not affect ringbuild

as long as the segments are still available at the TBM. Production is only stopped when needed

segments are no longer available. For EPB shields, the unavailability of muck cars is often a

critical bottleneck during advance.

Supply chains that are not dimensioned adequately result in interruptions in the sequential

production processes. These downtimes can be addressed through adequate planning. While a

superfluously powerful supply chain is not possible due to spatial, organizational, and most

importantly, economic constraints, an adequate dimensioning is desirable for reducing

downtimes to a minimum.

6. Modification of machine and logistic setup

Modifications are usually related to either unexpected geotechnical conditions or an

insufficient supply chain. Prominent examples concerning unforeseen soil conditions are the

installation of slurry pumps to cope with watery soils in EPB shields or the installation of

crushers to prevent blockages in slurry lines if fractured rocks are encountered. Such late

modifications are necessary to ensure the safety and functionality of the production processes.

Modifications of the logistic setup, however, are usually not indispensable. However, if the

intensity of disruptions exceeds a tolerable threshold, they might be justified. For example, if the

unloading of muck carts slows down the excavation process, the installation of a tunnel belt

might improve performance.

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7. Extraordinary events

This category includes all standstills due to issues not directly related to the construction,

such as those mandated by public authorities and owners (or their representatives), because of

settlement or heaving above the threshold value or quality issues with the tunnel lining. Other

examples are strikes, accidents, and natural disasters.

1.2.2 Addressing downtime in project planning

The general goal of all project management activities is the minimization of performance

losses and unproductive periods. In some cases, the reason for such downtime cannot be

remedied. A sound project planning convincingly addresses these downtimes with counter-

measures or at least accounts for buffer times. The previous section discusses seven reasons for

production standstills in mechanized tunneling. In the following section, they are classified

according to their potential to be addressed by planning prior to project implementation. This

section discusses three classes: unexpected and unavoidable downtimes, predictable but

unavoidable downtimes, and predictable and avoidable downtimes.

1. Unexpected and unavoidable downtimes

Some standstills can be neither predicted nor influenced by project managers. Thus, they

have little to no potential of being reduced. Such events include difficult geotechnical conditions

and extraordinary events. It can be argued that approaches based on observations during project

implementation enable a prediction of geotechnical changes. While the gathered results are

subject to uncertainty, the prediction often proves valuable. However, such procedures are

applied during project execution. This thesis focuses on the planning phase, which is the reason

for this classification. Consequently, such incidents should be excluded in comparisons and

performance evaluations of TBM projects. If the modifications of the setup are a reaction to

unexpected geotechnical conditions, the resulting downtime should be excluded as well.

2. Predictable but unavoidable downtimes

Extension of supply and disposal facilities and maintenance work are necessities and can thus

be planned accordingly.

Performance losses due to an extension of supply and disposal facilities are a result of the

method applied. For example, the extension of electric cables and the installation of passages are

regular processes of mechanized tunneling and thus unavoidable. But their recurrence

frequency can be modified to a certain extend. Thus, common practice aims to increase the

intervals of maintenance work in order to minimize the resulting standstills. The scheduling of

these planned downtimes should consider suitable track sections, with minimal risk of facing

instabilities.

[11]

Technical failures are not exactly predictable but their occurrence can be estimated during

planning. Since technical failures will occur eventually during the course of a project, they should

be addressed in project planning.

3. Predictable and avoidable downtimes

Problems resulting from a very sensitive or insufficient supply chain are frequently observed

on TBM projects. The production often stops because required material is not disposed timely or

capacities are not adequate. Thus, the potential maximum advance rate cannot be achieved or

sustained. In the case that major performance losses are the consequence, late modification of

machine and logistic setup are often economically advantageous. Such modifications usually

require a standstill of several days to weeks. These downtimes could actually be reduced by a

sound planning and a setup well suited for the encountered project conditions.

1.2.3 Discussion of common planning practice

The sequential character of the production processes and the associated dependencies

obviously determine the project performance. Additionally, the logistic processes in the supply

chain significantly influence the overall project performance [Bickel et al., 1996].

Mechanized tunneling is frequently compared to the stationary industry because of the

repetitive character of production and high degree of automatization. This might lead to the

conclusion that the timely delivery and disposal of materials is straightforward and does not

require in-depth consideration. However, the heavy construction operation in shielded

tunneling with all the uncertain project constraints features a high potential for production

disturbances caused by dependencies with the associated supply chain. Operational disruptions

might become so severe that a cascade of disturbance is triggered, which might in turn result in

a very long standstill time.

Tunneling by means of TBM has only limited flexibility during construction [Leitner and

Schneider, 2005]. Additionally, decisions concerning production and logistic must be made at

early stages with limited knowledge. Once a setup is chosen, because of unsatisfying

performance, the adaption is usually associated with a lot of effort and high additional costs.

Consequently, the thorough planning and evaluation of possible alternatives prior to project

execution is a must to ensure reliable determination of budgets and durations.

The common practice of planning is mainly focused on boring speed and the identification of

appropriate performance-relevant geotechnical factors. Estimation of boring performance

basically depends on the quality of the geological and geotechnical information collected for the

prediction. This data also supports the decision with regard to construction method and

machine type. Several approaches to determine boring performance have been published over

the years. The estimated boring performance is used as a key parameter for determining the

dimensions of supply chain elements and scheduling of associated processes. Duhme et al.

[2015a] have recently published a review on the commonly applied planning methods for

[12]

scheduling in the tunneling industry. The most commonly applied conventional planning

methods include Gantt charts, the critical path method (CPM), program evaluation and review

technique (PERT), vehicle-cycle diagrams, time-distance diagrams, and of course Excel sheets

based on experience.

However, it is frequently observed that assumptions and estimates for volumes, capacities or

durations to determine the supply chain setup are often not shared among project or company

members. Hence, the planning quality might vary significantly, depending on the planner’s

experience and gut-feeling. As a consequence, transparency is seldom achieved.

Additionally, the deterministic character of the tables and diagrams complicates the

assessment of uncertainties. A robust plan should include the consideration of uncertain

characteristics of both production and logistic processes. Unfortunately, conventional planning

tools abstract the processes and their interdependencies to keep the complexity manageable.

Every non-deterministic parameter should be calculated separately. In combination with

alternative project implementations, the number of calculations rises exponentially if

uncertainties are present. Thus, uncertainties of parameters often receive little to no

consideration. The comparison of alternative project implementations is often neglected or

minimalistic. Furthermore, deterministic approaches are not suitable for exclusively considering

resource deployment and neglecting performance issues due to coupled processes.

What is more, conventional approaches are not suitable for considering technical failures at

process-level and assessing their impact on production performance in a detailed manner. In

order to compensate for this issue, an additional time share resembling disturbances is often

added to process durations. These events must be considered for robust project planning.

Disturbances occur in both production- and logistics-related processes. The evaluation of a

project setup concerning robustness therefore requires the explicit modeling of

interdependencies between production and logistics.

In order to sum it up, conventional calculations and planning tools are very valuable for

approximations and first estimations but also have several downsides. They often lack

transparency, are limited in addressing uncertainty, abstract process interactions to a great

extent, and are not suitable for considering disturbances in a detailed manner. The uniqueness

of construction projects in terms of dimensions and project conditions further aggravates this

issue. The detailed planning process is a time-consuming and, more importantly, resource-

consuming task. Therefore, the transparent evaluation of alternatives in project execution is

quite often omitted due to the associated planning effort. As a consequence, processes are often

not investigated in detail prior to project implementation. The learning phase of tunnel projects

has a lot of potential for improvements, especially in the logistic processes. The efficiency of

these high-capital projects requires the holistic planning of production processes in

correspondence with related support processes.

[13]

1.3 Thesis outline

This thesis addresses the limitations of conventional planning tools and presents an approach

based on process simulation to model operational disturbances and assess their impact on the

general production performance of shielded tunneling projects. In this thesis, the production

and logistic processes are investigated in details. All relevant elements and especially their

process-relevant interdependencies are modeled. Special focus is put on the possibility to model

the cascading disturbances by means of detailed representation of the said interdependencies.

In Chapter 2, the related research is presented and discussed. Special focus is put on

simulation-based approaches for tunnel construction. An introduction to the most prominent

simulation frameworks for construction is provided. Furthermore, approaches to quantify the

boring performance by application of probabilistic methods or substitute modeling are

discussed. Simulation-based approaches to consider disturbances in construction projects are

discussed separately. A comparative discussion of relevant approaches for identification of

relevant research goals follows. The methodology of the presented approach toward the stated

research goals is presented in Section 2.4.

The applied methods and relevant background of the research are introduced in Chapter 3.

The presented approach is based on three general methods: simulation, formal modeling, and

modeling uncertainties. The methods are described and reference to additional information is

provided.

A major effort of simulation studies is the formal analysis of the system, as presented in detail

in Chapter 4. First, the scope of the system analysis is stated clearly. Following that, all relevant

aspects are modeled in a graphical modeling notation.

The modeling of operational disturbances is another major aspect of this thesis, and is

discussed separately in Chapter 5. Operational disturbances include disturbances directly

affecting the production, problems in the supply chain, and cascading disturbances. This

categorization is discussed in the chapter.

In Chapter 6, the acquisition of project data is described. Details are also given about the

processing of this project data in order to derive input data for simulation experiments. The

underlying case study is introduced.

Chapter 7 provides details about the implementation of the simulation framework. The

developed simulation approach is described in terms of structural modeling, behavioral

expression, their interaction in a project context, and the consideration of disturbances.

The developed simulation-based approach is illustrated in a case study presented in Chapter

8. The simulation experiments performed within this case study are designed to illustrate the

impacts of the disturbance categories on project performance. The verification and validation of

the presented approach is addressed here as well.

The thesis closes with final remarks in Chapter 9. A brief summary outlines the presented

approach. This is followed by a conclusion to discuss the approach concerning the postulated

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research goals. Finally, the last section discusses further fields of research and potential aspects

for application in industry.

[15]

2 Motivation

In Chapter 1, the author describes the deficiencies of conventional planning approaches.

These include a lack of transparency, limitation with regard to deterministic calculations, high

abstraction of processes and interdependencies, and fewer possibilities to consider

disturbances. In order to address these issues, the author has developed an approach based on

process simulation. It is generally agreed that simulation is a powerful tool to investigate

complex systems and support the planning of construction projects. Simulation provides the

possibility to transparently analyze complex systems and provides easy as well as sophisticated

methods to consider uncertainty.

In the following section, a review of approaches based on process simulation for the

construction industry is given. After an introduction to simulation-based approaches for general

construction operations, we take a detailed look on applications addressing the tunneling

domain. Discussing the state-of-the-art simulation-based approaches reveals the research gap,

which is addressed by this thesis. This is stated clearly in the research motivation and the

defined research goals. The methodology to achieve these goals is presented in detail after this.

2.1 Review of related research

The presented approach focuses on the application of simulation as a tool to support the

planning of mechanized tunneling operations. The following discussion on state-of-the-art

methods thus focuses on simulation frameworks for construction operations, especially the

tunneling industry. At first, the relevant applications of simulation in the other fields of the

construction industry are briefly highlighted. This is followed by general purpose simulation

frameworks for the construction industry, while their application in tunnel construction is

discussed separately. The determination of advance rates in uncertain geotechnical conditions

by application of surrogate models is discussed afterward. The literature review closes with

approaches explicitly addressing the simulation of disturbances of construction processes.

The underlying simulation strategies, i.e. activity scanning, process interaction, and event

scheduling, of the specific frameworks are not addressed. Details of simulation strategies can be

found in the works of Martinez [1996] or Banks et al. [2001]. The term “simulation framework”

is used hereinafter instead of software in order to account for differences in implementation,

technological foundation, computer design, programming language, and other criteria.

2.1.1 Simulation-based approaches for planning in the construction industry

Simulation is a well-established tool for factory planning and design in the manufacturing

industry [Rabe, 2008]. The term digital factory (also virtual factory) in particular relates to the

application of simulation (see Kühn and Kuhn [2006] and Bley and Zenner [2005]). It is

frequently used to evaluate the dimensions of machinery and associated buffers in production

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systems. The repetitive nature of procedures, intensive use of machinery and high output figures

support a completely simulation-based analysis. Consequently, the list of simulation frameworks

for stationary industry - commercial or otherwise - is rather long. Most of them focus on the flow

of materials and processing of related entities, i.e. pre- and post-products, in a production

network.

Other areas of business also make extensive use of process simulation, such as investigations

into business workflows, traffic management, supply chain management, as well as economy,

healthcare, and military. Abu-Taieh and Sheikh [2007] provide an overview of commercial

simulation frameworks and their field of application. Additional information is also given in the

works of Banks et al. [2001], Law [2014], and Swain [2015].

The construction industry, on the other hand, did not easily adapt process simulation as a

technique to support project planning and/or implementation. The uniqueness of construction

projects is usually considered the main hindrance. Construction projects are seldom similar to

each other in terms of dimensions, techniques, resources, construction sequences, and, most

importantly, boundary conditions. Unlike in the manufacturing industry, boundary conditions in

construction may vary over the course of project execution. This includes changing weather and

political influences as well as intermediary stages of the building itself. The usually long project

duration, going up to several years from first concept to completion, increases the risk of

changes. The intermediate states of construction are another hindrance. Auxiliary constructions

and reallocation of job site layouts are the rule rather than the exception.

This implies a highly individual effort to establish simulation models for construction

projects. Furthermore, the project is usually only conducted once and the established simulation

models must be adapted or re-developed.

Application of process simulation in the construction industry is often based on CPM methods

[Halpin and Woodhead, 1998]. Manual scheduling with CPM is usually reduced to deterministic

process durations [Kelley and Walker, 1959]. The manual identification of efficient and feasible

construction sequences is the most challenging task for projects with complex process

interdependencies, numerous constraints, and alternative resources. For this reason, the

evaluation of several sequences is often omitted and only a single sequence is planned. In

computer-aided applications, uncertain input data for durations or consumptions can be

modeled. In combination with randomized Monte-Carlo experiments, the uncertain aspects can

be considered to compute project schedules [Christodoulou et al., 2009]. Beran and Hromada

[2008] present a simulation that integrates costs with the project schedule and thus provides

the opportunity to visualize the financial development of a project. Koo et al. [2007] incorporate

dependencies between limited resources and replaceable processes to identify suitable

schedules. The developed algorithm also supports rescheduling to automatically generate valid

construction schedules. Zhang et al. [2008] developed an algorithm based on the activity

scanning simulation paradigm that especially addresses the time constraints of the process, such

as breaks, overtimes, and the handling of resulting preemption. König et al. [2007] demonstrate

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the similarity between scheduling in shipbuilding operations and that in construction operations

by applying constraint-based simulation. Beißert et al. [2008] use constraint-based processes

simulation to identify potential areas of schedule improvement. The consideration of various

constraints in schedules proved to be a most valuable aspect for improvements. Beißert et

al. [2010] developed a general concept to distinguish between hard and soft constraints in

process simulation applications. Hard constraints represent a necessity (e.g. technological or

time dependency) while soft constraints are merely desirable (e.g. dirty works first). The theory

of soft constraints enables planners to postulate several criteria, the fulfillment of which is

preferable but not absolutely essential. Consequently, some solutions might not be appropriate

in terms of soft constraints but still provide constructible valid schedules. However, the

weighted evaluation of such soft criteria provides a multi-objective optimization problem. In

order to solve these optimization problems, several optimization algorithms have been applied

to the concept of soft constraints (see König and Beißert [2009], Hamm et al. [2009], Hamm and

König [2010], and Hamm et al. [2011]).

Mikulakova et al. [2010] apply case-based reasoning to evaluate the fitness of the generated

schedules. Wu et al. [2010] combines pattern-based construction methods and constraint-based

simulation to improve the process of schedule generation for bridge construction operations. In

[Szczesny, Hamm, and König, 2012] and [Szczesny, Hamm, and Koch et al., 2012] an adjusted

recombination operator is introduced to influence the generation of new solutions in the

optimization process of the construction schedule analysis. This significantly reduces the

amount of invalid schedule solutions by purely random recombination, thus improving the

overall performance of the optimization. Szczesny and König [2015] have also developed an

approach on the basis of fuzzy set theory to reschedule a project based on actual information

from the supply chain.

In addition to the approaches based on CPM methods, the application of Petri Nets to analysis

of the schedules of construction projects has been researched over the years. For example,

Sawhney [1997] applies high-level Petri Nets to hierarchically decompose the construction

schedule and incorporates stochastic aspects to consider the risk and uncertainty in process

durations. Cheng et al. [2011] apply Petri Nets for the simulation of earth-moving operations.

The simulation-based analysis and optimization of scheduling problems provides a very

active field of research. The list of contributions presented above is far from exhaustive but still

highlights some interesting approaches.

2.1.2 General purpose simulation frameworks for construction operations

One of the main motivations to conduct simulation studies is the comparison of alternative

construction methods [Ioannou and Martinez, 1996]. This implies detailed models for decision-

making, which consider all possible construction elements and their intrinsic processes. Due to

the uniqueness of construction projects, different conditions must also be considered [Vanegas

et al., 1993]. Such approaches mainly study the intensive use of heavy construction machinery.

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The acquisition and utilization of heavy equipment are very costly and thus companies strive for

high efficiency. Furthermore, heavy machinery implies a large number of repetitive processes.

As a result, the expected benefit associated with simulation studies justifies their development

[Halpin and Riggs, 1992].

Unlike manufacturing applications, the field of established simulation frameworks for

construction is rather small and academic in nature. The most noted is the CYClic Operations

NEtwork (CYCLONE) framework developed by Halpin [1977]. Martinez and Ioannou [1999]

consider it to be the first simulation framework designed especially for construction operations.

Therefore, CYCLONE is presented in detail in the next section. Following this, an overview of

later frameworks for construction simulation is also given. While these frameworks differ quite

significantly with regard to implementation and usability, the influence of CYCLONE is obvious.

2.1.2.1 CYCLONE simulation framework

The CYCLONE simulation framework was introduced by Halpin [1977]. However, it is still

popular, as proved by recent publications like the work of Zahran and Nassar [2013] on

modeling pipeline operations. Other publications with a focus on tunneling operations are

discussed in Section 2.1.3.

CYCLONE was specifically developed to model cyclic and repetitive construction operations.

This early framework provides a graphical modeling methodology for computer simulation of

construction operations. The basic idea of CYCLONE is to model the order of work tasks and

processes. The required resources are seen as entities distributed through the static simulation

model. In order to keep the framework simple, only three graphical categories are distinguished:

1. Squares define an active task or process. A distinction is made between constrained and

unconstrained tasks.

2. Circles represent an idle state or delay in the work flow. They are usually the result of an active

operation.

3. Directed arrows indicate the flow of entities through the established simulation network.

This general idea was taken up by later approaches, which are discussed or mentioned in the

following, and is thus detailed at this point. The framework is based on six graphical elements.

Figure 2 depicts the fundamental CYCLONE elements [Halpin and Riggs, 1992].

Figure 2: CYCLONE elements based on Halpin and Riggs [1992]

ARROWCOUNTERQUEUECOMBINORMAL FUNCTION

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The active modeling elements are NORMAL and COMBI. Both elements are represented by

squares, to highlight the resemblance of the tasks in the CPM methods. They should be specified

by a distinctive label and the typical process duration. The NORMAL node describes an

unconstrained work task that can be executed as soon as any number of entitles arrive. The

COMBI node additionally requires the amount of resources to perform the modeled task to be

specified. Thus, entities are delayed until the required amount is available. A simple example is

the unloading of a delivery truck, which requires a crane (or something similar) and the truck

itself.

The QUEUE node is a passive modeling element that provides a waiting position for entities

stringently required prior to COMBI nodes. The dwelling time of entities depends on the arrival

rates of resources and the requirements defined in the COMBI node. When these requirements

are met, the QUEUE releases the entity.

The FUNCTION node is used to execute specific behavior within the network. Consolidation

and splitting of entities in particular are achieved with this element. The FUNCTION node thus

provides the possibility of generating more entities within a cyclic network during model

execution. This enables the modeling of the receptive operations that control model execution.

The COUNTER node provides basic functionalities to analyze the system performance and

utilization. It is also referred to as the ACCUMULATOR. This modeling element can be assigned to

either the passing entities (e.g. trucks) or a specific value that is related to the entities (e.g. 10 m³

per truck). This element is also used to finalize a simulation run.

The final graphical modeling element of the CYCLONE simulation framework is the ARROW

or ARC, which is used to define the flow of entities within the simulation model. Thus, it connects

the previously discussed elements in an unambiguous way.

2.1.2.2 Adaptions and further developments of the CYCLONE framework

The disadvantages and shortcomings of CYCLONE were identified quickly. The level of detail

of CYCLONE models is rather abstract and often not enough for sound decision support. Most

importantly, the differentiation of similar resources, i.e. by properties such as loading capacity, is

not possible. Furthermore, the state of a simulated process cannot be evaluated during its

runtime. The combination of these issues results in the inability to make dynamic changes

during runtime, based on the condition of processes or properties of resources. A lot of effort

was taken to circumvent these limitations by additional enhancements to the original CYCLONE

framework. These are presented in the following passage in chronological order.

The INSIGHT framework, developed by Kalk and Douglas [1980], focuses on a more

interactive interface. UM-CYLONE is another adaptation developed by Ioannou [1990]. Micro-

CYCLONE was designed to run on micro-computers [Halpin and Riggs, 1992]. Lutz et al. [1994]

applied the Boeing learning curve in Micro-CYCLONE to investigate the learning curve for

repetitive construction operations.

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Halpin et al. [2003] presented the browser application Web-CYCLONE to foster information

exchange and peer-collaboration. Huang [1994] introduced the DISCO (Dynamic Interface for

the Simulation of Construction Operations) framework, which is basically a pre- and post-

processor for Micro-CYCLONE. The DISCO framework allows graphical design of models and

enhances the visualization of results. Rong-Yau et al. [1994] demonstrated the application of

DISCIO to simulate construction of cable-stayed bridges.

Additionally, several extensions and features were incorporated into the original CYCLONE

framework itself, including those by Dabbas and Halpin [1982], Remold [1989], AbouRizk and

Halpin [1990], and Lutz et al. [1994].

Chang and Carr [1987] developed the RESQUE simulation framework, which is also based on

the concepts and functions of CYCLONE but which specifically addresses the limited possibilities

of resource-handling by application of the process description language (PDL). The overlaid PDL

enables the distinction between resource types within the same network area. This provides the

opportunity to distinguish between multiple types of loaders or trucks, for example, without

duplicating the network for each resource type as in the original framework. RESQUE also

enhances control functionalities during runtime.

An early object-oriented implementation of the CYCLONE concept is the Construction Object-

Oriented Process Simulation (COOPS) system introduced by Liu [1991]. This general-purpose

simulation framework addresses construction and manufacturing systems in a similar manner.

Similar to RESQUE, COOPS models are also developed by a graphical user interface and resource

distinction is possible. The object-oriented design enhances the possibility to specify resource

requirements and their interaction, as well as tracing resources throughout runtime.

Furthermore, the combination of resources at network nodes is possible which enables

modeling of production processes in greater detail. Finally, COOPS models enable the

consideration of work-shift calendars to simulate work breaks and holidays by preempting

activities. Liu [1995], for example, uses COOPS to model the construction of parking structures

with precast elements.

MODSIM, another object-oriented simulation framework, was developed by Oloufa [1993].

The framework emphasizes the correlation of construction elements and simulation objects.

Oloufa et al. [1998] also present an approach involving encapsulated simulation modules for

earth -moving operations. Users can connect the special purpose simulation (SPS) elements on a

graphical interface. The predefined simulation components represent several resources and

allow fast model development of earth-moving operations.

Construction Integrated Project and Process Planning Simulation (CIPROS) is an object-

oriented simulation framework developed by Odeh et al. [1991]. CIPROS enhances even further

the possibilities to specify resource characteristics of RESQUE. Hierarchical modeling of

elements is possible. CIPROS also provides the possibility of correlating construction plans and

design drawings with simulation objects. Thus, construction plans are analyzed with regard to

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quality and feasibility. A major disadvantage of CIPROS is the missing access to simulation states

during runtime.

Martinez [1996] developed the object-oriented simulation language STROBOSCOPE (STate

and ResOurce Based Simulation of COnstruction ProcEsses). The underlying simulation strategy

of SROBOSCOPE is, like the original CYCLONE framework, the activity cycle diagram method.

Additionally, STROBOSCOPE supports the three-phase activity scanning simulation strategy. The

graphical elements of STROBOSCOPE and CYCLONE are very similar. The major difference is the

possibility to extend the basic functionality of STROBOSCOPE through high-level programming

languages such C, C++, Pascal, or FORTRAN. This open software design enables simulation of

very specific behavior and application of external libraries. Furthermore, the simulation can be

influenced during runtime by user-defined programming code. The distinction of resources on

the basis of entity properties is also possible. EZStrobe [Martinez, 2001] is a trimmed version of

STROBOSCOPE that is solely based on graphical elements. It was designed to introduce novices

to simulation without need of detailed programming skills. The possibility to enhance the basic

functionality of STROBOSCOPE through high-level programming languages can be considered

the reason behind the success of the framework.

A second system is also very successful and frequently applied in the industry: the

SIMPHONY simulation framework introduced by Hajjar and AbouRizk [1999] to facilitate the

development of SPS applications. AbouRizk and Mohamed [2000] use the simulation of an earth-

moving operation for demonstration. Farrar et al. [2004] presents an SPS for road constructions

implemented in the SIMPHONY framework. Before SIMPHONY, the developers introduced three

standalone SPS applications. AP2 Earth [Hajjar and AbouRizk, 1996] is an SPS application for

modeling large-scale earth-moving projects. CRUISER [Hajjar and AbouRizk, 1998] was

developed to analyze aggregate production plants. The third application, CSD [Hajjar et al.,

1998], was especially designed to optimize dewatering of construction site. The SIMPHONY

framework was designed to overcome limitations and difficulties identified during the

development of these three tools. While especially designed for development of SP applications,

it also supports general purpose constructs similar to CYCLONE. SIMPHONY also features an

object-oriented design and access to higher programming languages like Visual Basic for

Applications (VBA). The original SIMPHONY framework has continuously been enhanced, up to

the present date. The latest version is called Simphony.NET and additionally supports the

application of C# code. Also, Simphony.NET can be applied in High-Level Architecture (HLA)

frameworks [AbouRizk and Hague, 2009]. Recently, the Runge-Kutte algorithm has been

implemented to enable dynamic simulation as well. Hierarchical modeling, which results in high

flexibility for model development, is also possible. The graphical user interface provides

elements for both input and output analysis.

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2.1.3 Applications of process simulation in the tunneling industry

Two approaches for process simulation of tunnel constructions are outstanding with regard

to actual application to support construction projects and the associated continuity of research

effort. These two approaches are SPS for utility tunneling, developed under the supervision of

Prof. AbouRizk at the University of Alberta in Edmonton, Canada and the Decision Aids for

Tunneling (DAT), developed under the supervision of Prof. Einstein at the Massachusetts

Institute of Technology in Cambridge, USA. This section completely describes these two

approaches, followed by an overview of additional less popular approaches.

2.1.3.1 SPS for utility tunneling

In the works of Hajjar and AbouRizk [1999] and Hajjar and AbouRizk [2002], the SIMPHONY

simulation framework is introduced to facilitate the creation of SPS applications and support

users with limited simulation expertise through specialized user interfaces and predefined

modeling elements. Several SPS tools to support tunnel construction have been developed in

Canadian research efforts.

The SPS for utility tunneling, developed by Ruwanpura [2001], is implemented in SIMPHONY

and was first introduced in the work of AbouRizk et al. [1999]. It is a well-established tool in the

city of Edmonton, Canada to support utility tunneling operations. It was constantly enhanced at

the University of Alberta in Edmonton. Ruwanpura and AbouRizk [2001] implemented first-

order Markov chains with two states to determine the occurrence of soil transitions based on

borehole data. The two states in the Markov chain resemble soil of Types A and B respectively.

The gathered information is then used as input data in the simulation application. The basic

concept is enhanced and further detailed in the works of Ruwanpura et al. [2004].

The extensive use of the SPS for tunneling by the city of Edmonton is probably the main

driver for the improvement and enhancement of this simulation tool. Ruwanpura et al. [2001]

document the first improvements of the original SPS, which were based on the requirements of

operational engineers in the city of Edmonton. The authors present the SPS tool in great detail

and highlight the possibilities of one-way and two-way tunneling, the estimation of associated

costs, and project durations in presence of input uncertainty. Furthermore, the SPS supports the

simulation of material transport from the TBM to the surface by means of train-bound muck

carts. In calibration experiments, the authors identified the key parameters that have significant

influence on simulation results. These parameters include the boring rate, the ringbuild

duration, the swelling factor of the soil, and the number and capacity of deployed muck carts.

After calibration and implementation of change requests, the SPS for tunneling was applied to

plan for the South Edmonton Sanitary Sewer tunnel, driven with a shielded TBM, as a real case

project [Ruwanpura et al., 2001]. During project execution, the requirements for the sewer

tunnel changed and the SPS for tunneling was used to evaluate project alternatives, including

tunneling in two directions simultaneously.

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Fernando et al. [2003] presents three projects in which the SPS for tunneling was applied by

the city of Edmonton’s construction department. The engineers used the simulation tool for the

estimation of project duration and the associated costs of refining the resource deployment, i.e.

the number of trains and carts applied. The selected case studies are the previously described

South Edmonton Sanitary Sewer tunnel (2.5 km long), the Calgary Trail Interchange Tunnel (510

m long), and the North Edmonton Sanitary Trunk tunnel (1.6 km long).

Chung et al. [2006] applied Bayesian updating to improve the quality of input data for the

North Edmonton Sanitary Trunk tunneling project. Al-Battaineh et al. [2006] applied the SPS for

tunneling to plan the construction of the Blencoe Tunnel in Calgary, Canada.

Zhou et al. [2008] enhanced the SPS for tunneling to model tunnel shaft constructions. The

enhancement was applied during the construction of a new 2.3 km long tunnel as part of the

North Edmonton Sanitary Trunk tunneling system. The SPS is able to simulate the excavation of

both circular and rectangular shaft cross-sections. The modeling allows for the consideration of

sump excavation, slab pouring, safety wall installation, and excavation of undercut or tail tunnel.

With development of the COSYE (COnstruction SYnthetic Environment), an HLA framework

developed by AbouRizk and Hague [2009], the SPS for tunneling was enhanced to support 3D

CAD information and visualization of simulation experiments [Yang et al., 2010].

An SPS specifically addressing the logistic aspects of supply chain in tunnel constructions is

presented in the works of Ebrahimy et al. [2011a] and Ebrahimy et al. [2011b]. The SPS is

referred to as SIMPHONY Supply Chain Simulator (SSCS) and includes 12 elements to model

demand, production, distribution, inventory stocking, and transportation of materials.

Furthermore, the SSCS enables the consideration of different forecasting and information-

sharing policies. The SSCS was applied in combination with the SPS for tunneling at the Glencoe

Tunnel project in Calgary, Canada.

Moghani et al. [2011] enhanced the SPS for tunneling to model sequential excavation with

either shotcrete or rib and lagging as supporting installations. The enhancement was motivated

to support a construction of two parallel drives (764 m long) as an extension of Edmonton’s

public transport system.

Xie et al. [2011] developed a monitoring system, as part of the COSYE, to collect input data for

simulation experiments conducted with the SPS for tunneling. The approach was tested during

the construction of a 3.7 km long sewer tunnel for the North Edmonton Sanitary Trunk tunneling

system. The monitoring includes the observation of production progress and interruptions. The

application of the SPS for tunneling to support the planning process of this project is detailed in

the works of Al-Bataineh et al. [2013]. The simulation tool was used to evaluate 10 alternative

project implementations.

As the city of Edmonton is located in Canada’s colder areas, the extreme weather conditions

frequently affect tunnel constructions. In order to consider this uncertain aspect of construction

operations, a corresponding approach is presented in the research of Shahin et al. [2013]. The

authors identified several reasons for reduced productivity due to cold weather. This comprises

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stoppage conditions, e.g. if temperature drops below −40°C, and reducing influences, e.g. high

winds that prolong the hoisting cycles or reduce mucking capacity due to soil sticking/freezing

in the carts. A stochastic weather generator simulates these conditions.

Werner and AbouRizk [2015] present the simulation of disturbances affecting the

performance of a TBM for the SPS for tunneling. Due to its high relevance to the presented

approach, this publication is discussed in more detail in Section 2.1.5, where disturbance-related

approaches are especially highlighted.

2.1.3.2 Decision Aids for Tunneling

The second approach, regarded separately, is formed by the Decision Aids for Tunneling

(DAT). Unlike the SPS for tunneling and other approaches discussed in the next chapter, the DAT

are not based on CYCLONE or an adaption of this framework. DAT are implemented in the

simulation framework SIMSUPER, which is used for the actual processing, and the graphical

user-interface SIMJAVA as pre- and post-processor [Indermitte et al., 2015]. DAT were

developed independently and the first reference to a pioneering approach is given by Vick and

Einstein [1974]. Consequently, the origin of DAT is very likely the first published approach to

estimate the durations and associated costs of tunneling projects by application of process

simulation. The early concept has been continuously enhanced under the supervision of Prof. H.

H. Einstein at MIT and is first referenced as DAT in the work of Einstein et al. [1992].

Intermediate results were published by Salazar and Einstein [1986] and Einstein et al. [1987].

DAT must be understood as a specialized tool for tunneling projects. Over the decades of

development, several areas of project management have been addressed [Einstein, 2004]. DAT

support both TBM tunneling and tunneling with the NATM method [Min et al., 2008]. Einstein

[1996] highlights the consideration of uncertainties in the geological profile and assessment of

associated risks. Sinfield and Einstein [1996] evaluate the modification of (micro-) tunneling

technologies for TBM operations in terms of their potential for cost and time savings. Apart from

the consideration of uncertainties in soil conditions, the simulation of project resources is a main

aspect of DAT [Einstein et al., 1999]. The possibility of updating the simulation with real-time

data, based on Bayes’ theorem, is presented in the works of Haas and Einstein [2002].

DAT were successfully applied at the Gotthard Base Tunnel and the Lötschberg Base Tunnel

[Einstein et al., 1999]. In Korea, DAT were used to support planning and project management of

the Sucheon tunnels, two 1.9 km long road tunnels constructed by the drill-and-blast method

[Min et al., 2008]. Ritter et al. [2013] present the simulation of excavation material-handling,

including investigations for potential reuse or landfill deposit. This enables DAT to also consider

environmental and economic issues related to sustainable tunnel design.

2.1.3.3 Other simulation approaches in tunnel construction

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Previously, two major contributions were discussed separately due to their continuous

development over several decades and acceptance in the industry. This chapter discusses some

other approaches based on process simulation to support tunneling projects.

A very early approach implemented in the CYCLONE framework is found in the works of

Touran and Asai [1987]. The approach analyzes the effect of unexpected soil variations by

reducing the advance rate, as well as the influence of lining on production performance. The

approach is based on deterministic assumptions, and investigates correlations between

tunneling performance and supply chain resources, i.e. deployed trains.

Halpin and AbouRizk [1991] provide a comparison of two simulation frameworks. The

simulated system used is the liner plate tunneling method. The system is implemented in Micro-

CYCLONE and in the general purpose simulation language SLAM II. The generated results are

identical but the authors highlight the user friendliness of CYCLONE, which is a result of the

graphical user interface.

Weigl [1993] presents in his PhD thesis a simulation model implemented in the CYCLONE

framework to estimate tunneling performance. The model is based on a detailed analysis of the

associated processes at two tunneling projects. Process durations are determined very

thoroughly. Sadly, the author neglects process disturbances due to the increased effort of

generic implementation to simulate the occurrences of different standstill causes. Instead of

considering disturbances, the durations of unplanned standstills are neglected for validation

purposes.

Nido et al. [1999] developed a CYCLONE simulation model for micro-tunneling operation. The

model is used to evaluate factors affecting productivity. A micro-tunneling project in Ohio, USA is

used as a case study. The findings show that the soil composition has the most impact on the

operation and can account for 50–60% of overall project costs. Associated resources are

considered in the simulation model. The effect of process disturbances, however, is not

investigated in detail.

Likhitruangsilp and Ioannou [2003] implemented an approach in the STROBOSCOPE

simulation framework to evaluate the tunneling performance of applicable tunnel construction

alternatives. The results of the approach include probability distributions of project durations

based on the advance rates of previous projects and tunneling costs of possible alternatives.

Uncertainties with regard to input data are assessed subjectively but not addressed

stochastically. In the work of Ioannou and Likhitruangsilp [2005], the approach is enhanced to

consider the limited resources deployed in the said alternatives. Both contributions are

demonstrated with the data derived from the Hanging Lake Project in Colorado, USA.

Marzouk et al. [2008] developed an SPS tool for tunneling called TUNNEL_SIM. The tool

allows the estimation of project duration and associated costs. The simulation of open and

closed excavation in rectangular and circular cross-sections is possible, albeit at a rather

abstract level. Several ground-supporting techniques can be simulated as well [Abdallah and

Marzouk, 2013]. The tool also features decision support based on fuzzy theory for transparently

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ranking possible alternatives. The evaluation of micro-tunneling operations is possible as well

[Marzouk et al., 2010].

[Thanh Dang, 2013] presents an SPS for micro-tunneling operation implemented in the

AnyLogic simulation framework [XJ Technologies, 2015]. The approach is demonstrated by post-

project simulation of a project in Recklinghausen, Germany [Thanh Dang et al., 2013]. It is

possible to consider varying soil conditions, uncertain process durations, and resource

allocations. A graphical user interface supports the application. The approach is based on a

thorough system analysis formalized in a standardized graphical modeling notation. The explicit

simulation of disturbances is not addressed.

Liu et al. [2010] present a simulation model for hard rock TBM operations. The approach is

implemented in the CYCLONE framework. The level of detail in the operational process remains

abstract but includes several material-handling operations. The removal of muck is especially

addressed and the authors use the simulation model to identify the optimal number of deployed

muck cars per tunnel train as well as the numbers of trains. The location of switches can also be

considered in the simulation model. The approach is demonstrated by a 20 km tunneling project

in China. In the work of Liu et al. [2015], the approach is coupled with an expert system to

account for geologic uncertainties and associated risks for production performance. The

elements, parameters, and assumptions of the original CYCLONE model are thus adapted

according to varying geotechnical conditions. The adaptive CYCLONE approach is successfully

demonstrated by a 16.7 km long tunneling project in southwest China.

2.1.4 Application of surrogate models for the estimation of TBM performance

This chapter introduces the application of surrogate models to predict tunneling

performance. The approaches are not based on process simulation, but their close similarity to

the presented approach requires them to be considered in a literature review.

In general, two types of surrogate modeling paradigms are discussed, both of which generate

results while considering uncertainty and associated stochastic effects. Empirical approaches

that apply techniques to generate extrapolations of observation data are the first category. The

second category is provided by surrogate modeling approaches based on analytical models, in

combination with observation or estimation data. The underlying analytical models vary to a

certain extent. The specific modeling techniques applied in the discussed approaches are not

explained in detail at this point.

The application of surrogate modeling is restricted to hard rock tunneling for a simple

reason. In mixed or soft ground conditions, the influencing parameters are manifold and

analytical models were not convincing yet [Tarkoy, 2009]. As opposed to soft grounds, the

penetration in hard rock is dependent on rock parameters. Over the years, a lot of research

effort has been made to identify suitable correlations and push their limits in mixed conditions

(see Schneider and Leitner [2003], Sapigni et al. [2002], Yagiz [2006], Hamidi et al. [2010],

Hassanpour et al. [2010], Hassanpour et al. [2011], and Paltrinieri et al. [2016]).

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One of the most commonly applied analytical models to predict performance in hard rock

tunneling is the Colorado School of Mines (CSM) model [Ozdemir, 1977]. The second well-

established model to predict hard rock tunneling performance, developed at the University

Trondheim (NTNU), is based on empirical observations but not analytical correlations. A

comparison between of the CSM model and the NTNU model can be found in the work of

Rostami et al. [1996].

Benardos and Kaliampakos [2004] developed an approach based on artificial neuronal

networks (ANNs) to predict the future advance rates in a Greek metro project. To generate

results, ANNs are trained by being fed with historic observations as input data with known

results. Therefore, the method can only be applied after the project has started and applicable

data is generated. As the input data trains the network, the amount of observation data increases

and the quality of results improves. ANNs also allow pattern recognition in the observation data

and can thus be used to derive correlations. Consequently, ANNs are able to adapt to changing,

and especially unexpected, conditions. This feature is probably the reason for the large number

of ANN-based approaches for tunnel constructions. The approach developed by Benardos and

Kaliampakos [2004] is limited to the consideration of geological and geotechnical site

conditions.

Several hybrid approaches have been published that aim to overcome the limitations of ANNs

by combining them with a suitable complement. Grima et al. [2000] developed a neuro-fuzzy

approach to estimate penetration rates. The approach combines ANNs to address numerical

issues, and fuzzy theory to solve logical aspects. The observations of 640 tunneling projects,

provided by Nelson et al. [1994], are used as input data.

Simoes and Kim [2006] developed both a rule-based (Mandami model) and a parametric-

based (Sugeno model) fuzzy logic modeling approach to anticipate TBM performance. Both

algorithms were tested with the data derived from three hard rock drives to correlate rock mass

properties and the utilization factor. The computed results were compared with field data. The

implemented Sugeno model generated more accurate and smoother results and is thus

recommended as a pre-planning tool.

Yagiz and Gokceoglu et al. [2009] highlight the difficulties of using linear prediction models in

statistical analysis in the presence of a high degree of uncertainty and numerous influencing

factors and constraints associated with tunneling operations. The authors thus developed a

hybrid approach based on ANNs and non-linear multiple regression models. This enables the

non-linear consideration of multivariable parameter correlations to derive performance

estimations based on recent observation data. The parameter identification and case study are

based on the research carried out on a completed water-tunneling project in the city of New

York, USA [Yagiz, and Rostami et al., 2009].

Gholamnejad and Tayarani [2010] also applied ANN modeling to estimate penetration rates

in hard rock drives. The underlying database includes observations from three tunneling

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projects in the U.S., Iran, and Ethopia. The best results of ANN were compared to the real

observations and gathered with a five-layered ANN.

Lau et al. [2010] combined ANN with a radial basis function to compute next-cycle

performance in drill-and-blast operations. The approach incorporates critical states of the

operation in relation to geotechnical or operational influences. The available project recording

systems are used to update the algorithm after each cycle.

Yagiz and Karahan [2011] propose a heuristic approach to estimate TBM performances by

application of particle swarm optimization (PSO). The authors claim that heuristic algorithms

are superior to ANNs or fuzzy theory due to their abstract implementations. However, the

conclusion of the presented application example is that best results are gathered when the

observation data of the completed project is used as input data. The approach is therefore

applicable only in post-project analyses.

Leu and Adi [2011] propose the combination of hidden Markov modeling (HMM) with ANN to

address the high degree of uncertainty in underground operations. This hybrid approach

actually aims to support decision-making by forecasting geotechnical conditions based on recent

observation data.

Petroutsatou et al. [2012] applied ANNs to investigate road tunnel constructions and provide

an approach for early cost estimation. The approach is based on the evaluation of geological,

geometrical, and resource-associated parameters affecting temporary and permanent support

installations and total project cost.

Moore and Pham [2012] developed a hybrid approach capable of context-aware decisions in

real-time computation models. The approach combines ANNs with self-organizing maps (SOM)

that enable both supervised and unsupervised training of the network. This allows the approach

to respond to unexpected incidents, induced by unforeseen geology for example, and to adapt

performance and risk estimations for the operation. The approach was successfully tested in the

post-project experiments of Asian tunneling operations.

Špačková and Straub [2013] present a sophisticated approach that involves applying dynamic

Bayesian networks (DBN) to assess tunnel construction performance. The approach especially

addresses the occurrence of extraordinary events, causing severe performance loss. The input

parameters are derived by analyzing three conventional tunneling projects and testing an

artificial application example [Špačková et al., 2013]. Špačková and Straub [2013] successfully

apply the approach to a NATM project to demonstrate its general qualification.

2.1.5 Research addressing disturbances of construction operations

Very few approaches explicitly address the occurrence, origin, and effects of disturbances.

Most approaches deal with disturbances at the management level and consider this aspect of

operation by adding downtime ratios to productive times. This is either done implicitly by

increasing the productive process durations directly or explicitly by considering a “disturbance

process.” Due to the limited number of approaches available, the review includes applications

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for tunnel and general construction operations, as well as approaches that are not based on

process simulation.

Leitner and Schneider [2005] propose a categorization of operational process and associated

downtimes. The authors determine the total cycle time by adding the boring and support

durations. They further classify planned (e.g. maintenance, tool change, exploratory

measurements etc.) and unplanned downtimes. The authors name issues with TBM, backup

trailer, tunnel transport, and others as reasons for unplanned downtimes. The basic idea of the

approach presented by Leitner and Schneider [2005] is the iterative calculation of time quotas

for both planned and unplanned downtimes. These quotas are added to the total cycle duration,

which is also calculated iteratively. Every iteration, all available quotas are updated with new

observation data. The approach is solely tested with spreadsheets, resulting in a lot of manual

effort to update and calculate data. Reference for implementation in computer-aided expert

systems or similar is not found.

Lu and Chan [2004] enhanced the simplified discrete event simulation approach (SDESA) to

include downtimes of construction operations. The authors demonstrate the approach with the

earth-moving example and compare SDESA results with the results generated by a CYCLONE

model implemented in the SIMPHONY framework. The artificial case study considers resource

breakdowns, absence of resources, and activity interruption, especially lunch breaks. As a real

case study, the authors present a model of hoist and barrow operations.

Gehbauer et al. [2007] developed an approach to simulate disturbances on high-rising

construction sites. The authors gathered data by fulltime observation of a jobsite, with a high

level of detail about documented disturbances. Based on the observations, a disturbance

database has been derived which allows parameterization. The simulation approach is based on

CPM and the predecessor-successor relationship of construction processes. The implementation

includes the dependencies of logistic processes and the attributes of the deployed personnel.

However, the approach requires the user to define disturbance scenarios that are then

simulated.

A management tool to assess the risks in open mining projects was developed by [Chinbat

and Takakuwa, 2009]. The research was motivated by the requirement for cost reduction in a

Mongolian mining plant. The authors performed a structured risk analysis and derived input

variables and dependencies. The gathered information was abstracted and computed by multi-

objective linear programming.

Christodoulou et al. [2009] present a very interesting approach based on the entropy of a

project as a measure for the disorder present in the system, i.e. the construction project. Based

on this entropy, the authors assess the tendency of the project to deviate from the designated

track. The developed approach aims to adapt resource allocation and scheduling of resource-

constrained processes based on the entropy analysis.

[Hong et al., 2009] used event tree analysis for risk assessment of underwater operation of

EPB shield machines. The analysis aimed to quantify associated risks at preliminary stage. With

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the event-tree analysis, the authors investigated the cause and effect of possible incidents and

suggest countermeasures. The occurrence of events was derived by the investigation of

empirical data of completed projects, design reports, and case studies. Furthermore, numerical

modeling and hydrological analysis were conducted to support the findings and derive a

quantitative risk assessment. The research provides details about the derived event trees.

Implementation of quantitative risk assessment in a simulation framework is not referenced.

Jafari et al. [2009] investigated the potential risks of TBM operation for human employees.

The research applied machinery failure mode and effects analysis (MFEMA) to identify failure

incidents in TBM operations. The authors investigated the mechanic, hydraulic, pneumatic, and

electric systems and assessed their danger in terms of personnel safety. Based on the FEMA

findings, a risk matrix was developed with regard to probability, severity, and chances of

detection. Computer-aided evaluation of the findings is not mentioned.

Alarcón et al. [2012] investigated the correlation of equipment replacement and machine-

failure costs in tunneling operation. The authors conducted a simulation-based study of five

drill-and-blast operations to derive quantitative information about consequential costs of

equipment availability and replacement. The approach is economically oriented and does not

investigate the probabilities of or reasons for failure of tunneling machinery.

Frough and Torabi [2013] present an approach to anticipate the TBM downtime of hard rock

drives based on encountered geotechnical conditions. The approach uses rock engineering

analysis as an analytical tool to correlate downtimes and geotechnical aspects. The authors

gathered information about three hard rock drives and tested the approach successfully to

generate robust estimations of hard rock TBM downtimes.

Werner and AbouRizk [2015] present a simulation model to specifically address disturbances

in TBM operations. According to the authors, the approach is an enhancement of a model

developed at the University of Alberta as part of an unpublished manuscript. The model was

developed with the general purpose simulation elements of the SIMPHONY framework. Based on

the evaluation of a municipal tunneling project of 700 m, the authors identified 10 reasons for

downtime and gathered the corresponding data. The developed model is rather abstract. The

whole tunneling operation is modeled in a single SIMPHONY network instead of modeling the

various elements associated in TBM operations in detail. The occurrence of disturbances is

modeled separately for each considered reason for downtime. This further increases the

complexity of the developed approach. If a failure occurs, the resource TBM is preempted for

process execution and a project downtime is simulated. The possibility of overlapping failures or

cascading effects is not discussed.

While Werner and AbouRizk [2015] modeled failures in quite a structured approach, the

difficulties of explicitly modeling process interruptions in CYCLONE-based simulation

frameworks have been vividly demonstrated by Lu and Chan [2004]. The authors compare

simulation results by considering operation downtimes caused by resource breakdown and

activity interruptions. The results are generated by a SDESA model and a CYCLONE model

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implemented in the SIMPHONY framework. The authors depict the CYCLONE model both with

and without considering operational disturbances. The model presented by Lu and Chan [2004],

which does not consider operational disturbances, features eight nodes, whereas the model

considering disturbances features 26 elements, implying a three-fold increase of nodes. This

highlights the high degree of abstraction and increased modeling effort required for considering

operational downtimes in network-based simulation frameworks. Additional expert knowledge

of the simulation framework is also necessary. The approach presented by Werner and

AbouRizk [2015] has a similar dimension. The results are sound and helpful at a management

level but the expert knowledge required for understanding the modeling concept is rather high.

2.2 Research motivation

The planning of tunnel construction is at present mostly based on experience and

deterministic planning tools. The application of computer-aided engineering (CAE) methods for

construction planning is rising, but most applications (academic or not) continue to focus on

investigations and control of the actual boring process or determination of the advance rate in

relation to uncertain soil formations. Deterministic planning approaches are not suitable for

holistically addressing the complex correlations inherent in tunneling projects. The abstraction

and complexity of the planning tools and the reliance on experience hinder transparent

decision-making. Production or logistic processes are naturally dynamic in nature and require

dynamic representation in the planning process. This is not possible with deterministic

approaches, which have two basic drawbacks. First, the calculation of process with average

values neglects the risk of potential performance losses in coupled or dependent processes.

Second, the consideration of resources and their potential to influence production can only be

done implicitly through Gantt charts or deterministic tables. The consideration of randomly

occurring disturbances is also not possible.

Simulation is widely believed to be a powerful tool for the planning and analysis of complex

systems and is suitable for overcoming these drawbacks. The level of detail and investigation

scope can be adjusted to the planners’ needs. Alternatives can be evaluated and results are

presented transparently. The identification of critical elements and bottlenecks is possible. The

influence of disturbances on the overall system can be determined. The decision-making process

is thus supported by a transparent planning tool.

The following section summarizes the approaches identified in the literature review. Over the

past decades, major efforts have been made to introduce simulation in the construction industry.

Unlike in manufacturing industries, there is no wide variety of commercial frameworks with

predefined simulation components for the various construction activities available. As stated

earlier, three frameworks are most commonly used for processes simulation of construction

operations: CYCLONE, STROBOSCOPE, and SIMPHONY. Fundamentally, the concept of all three

frameworks resembles the queueing theory, where entities flow through networks comprised of

nodes, forks and queues. Depending on the modeler’s views, entities may vary in terms of

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construction material, heavy equipment, processes, activities, and even intermediate

construction phases. Correspondingly, the network nodes may represent (process) stations or

equipment. It is possible, for example, to model all construction process performed for a specific

floor through network nodes and consider the distinct floors to be built as entities propagated

through this network. Depending on the point of view, detours and workarounds are necessary

to model complex or specific behavior. Thus, the level of abstraction, already identified in the

early CYCLONE framework, remains high in STROBOSCOPE and SIMPHONY. The resulting

models are complex in structure and nontransparent in representation. The verification of

models or comparison with conceptual models is only possible if one has a great deal of

experience in the applied simulation framework.

Modularizing the specific areas of the network helps to reduce the overall complexity and

facilitates the consideration of alternatives. However, the model is still based on, and more

importantly, controlled by the flow of specific entities. Therefore, the exchange of entities

between two partial modules might be restricted to a certain kind of resources or be bound to

additional modeling effort. Either way, a lot of simulation-specific knowledge is needed, even

though some researchers claim otherwise. The level of abstraction in activity-based networks is

high. Consequently, verification with conceptual model descriptions is difficult and complex.

AbouRizk et al. [1992] state that acceptance in the construction industry can only be achieved

if simulation models are presented in a simple and graphical context. AbouRizk et al. [1992]

believe that analytical and theoretical formats are not accepted by stakeholders from the

construction industry. While all of the mentioned simulation frameworks provide a graphical

user interface, the model elements and the corresponding methodology limit the modeler to a

highly abstract system description. Even experts who are familiar with the simulation

framework need a lot of introduction, documentation, and time to just comprehend a

sophisticated model, let alone verify its quality or accuracy. Consequently, stakeholders are

seldom able to judge model quality. This could be one reason that application of simulation in

construction industry is still limited and mostly of an academic nature [AbouRizk, 2010].

Additionally, the standard functionality of simulation frameworks is often not sufficient to

represent complex systems in detailed simulation models. The requirement to enhance the

functionality of standard modeling elements and influence their behavior during runtime by

through user-specific programming is often found in literature. As described earlier, this

enhancement is among the first adaptions of the early frameworks. Simphony.NET and

STROBOSCOPE provide access to higher programming languages with additional flexibility and

enhanced functionality.

While the specification of resource properties is possible, SIMPHONY does not support the

naming of such entity variables. All entities feature a general data field to model specific

attributes. However, the identifier of this data field is numeric, and thus labeling is not possible.

A short example based on an earth-moving project shows that this lack of transparency is a

major disadvantage in modeling. Two different trucks, with different loading capacity, are

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applied in the project. The loading capacity should be modeled as a relevant attribute of the

truck. The entity requires the said data field to specify such attributes. The identifier of the data

field is a numerical value. The first entry of the data field could be specified, for example, as the

brand of the truck, the second entry as some kind of identifier (e.g. license plate), and the third

entry the loading capacity in tons. These attributes are associated with the numbers 1, 2, and 3.

Usually, the attributes of several entities must be given to model specific behavior. If the loader

has to be specified as well, the order of attributes for this entity could differ. The applied loader

might not feature a license plate. The second entry of the data field is thus either left blank or

occupied by a completely different property. This short example highlights the high chance of

mistakes in relationship declaration and the associated efforts to verify the modeled

dependencies.

To sum it up, the following table presents the most relevant approaches in relation to the

addressed deficiencies. The selected approaches are the SPS for tunneling and DAT, as well as

the approaches presented by Weigl [1993], Liu et al. [2015], and Thanh Dang [2013]. The

approach presented by Thanh Dang [2013] actually focuses on micro-tunneling but is

nonetheless listed as it has some very interesting aspects. The approaches are qualitatively

assessed by the author in terms of seven aspects in Table 1. These categories are level of detail,

reusability, transparency, holism, process uncertainty, soil uncertainty, and disturbances, all of

which are described briefly afterward.

Table 1: Comparison of simulation-based approaches concerning relevant aspects

SPS for

tunneling DAT Weigl [1993]

Liu et al. [2015]

Thanh Dang [2013]

Level of detail Low Low High Low High

Reusability High High Very low Very low Very low

Transparency Low Very low Low Low High

Process uncertainty High High Low Medium Medium

Soil uncertainty High High Low High Low

Holism Medium Medium Low High High

Disturbances Blurred Not addressed Blurred Not addressed Blurred

A low level of detail implies that the approach is focused on management issues and neglects

interdependencies. These approaches are not suitable for expressing dependencies between

production, logistic processes, or resources in case disturbances occur. Approaches with an

objectively high reusability can be easily adapted to future project requirements. The aspect of

transparency reflects the possibility of assessing the approach without or with limited expert

knowledge. In the author’s opinion, the CYCLONE-based frameworks have low transparency as

they require a lot of experience to comprehend the modeling strategy of others. A standardized

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formalization of the system with a matching implementation is considered highly transparent as

it facilitates model verification. The consideration of uncertainty is divided into process

uncertainty and soil uncertainty. Process uncertainty addresses the possibilities of considering

uncertainties in duration, occurrence, rate, speed etc. Soil uncertainty, on the other hand,

indicates whether approaches are capable of considering heterogeneous soil formations and

simulating the resulting impact on production-related processes. The criterion holism addresses

the completeness of the performance-influencing factors. This criterion especially addresses the

interdependencies between production and logistic processes. However, holism is put in context

with the level of detail to assess its quality. Finally, the aspect of disturbance states the

possibilities to address process disturbances and assess the resulting impact on performance.

The table shows that the level of detail is not satisfactory in any of the approaches, except the

approach presented by Weigl [1993] and Thanh Dang [2013]. Reusability, on the other hand, is

addressed intensively by the SPS for utility tunneling and DAT. This can be explained by the fact

that the other approaches represent isolated efforts and have not undergone comparable

durations of development. The transparency of almost all the approaches is considered low or

very low. While all approaches present a thorough system analysis to a certain point, only the

approach by Thanh Dang [2013] provides a standardized system description that easily matches

the implementation. Consequently, practitioners and industry stakeholders face difficulties in

validating the correctness of the other approaches, as a lot of simulation specific knowledge or

programming expertise is required. Almost all approaches support the consideration of process

uncertainty. However, this is a key feature of simulation-based approaches and thus mandatory.

Modeling soil uncertainties on the other hand is not supported in a sufficient manner by all the

compared approaches. SPS for utility tunneling and DAT provide extensive possibilities to

consider geotechnical influences. Liu et al. [2015] presented a context-aware adaptation that

directly influences the simulation results. With the criterion holism, the presented comparison

qualifies the interaction of production and logistic as conventional approaches mainly focus on

the boring process and neglect other influences. The approaches discussed here feature a high

degree of variation concerning this criterion. Nevertheless, the majority of the compared

approaches address the dependencies between production and logistics. The last aspect

evaluates the explicit simulation of process disturbances. All of the compared approaches

consider this inherent aspect of performance estimation with unsatisfactory quality. Out of the

five compared approaches, only three address disturbances at all, and even they present

disturbances as an additional duration to productive processes and thus blur the effect. It must

be stated at this point that Werner and AbouRizk [2015] have discussed the possibility to

consider disturbances to the SPS for utility tunneling. However, the approach is considered

insufficient by the author, as it was specifically developed for a certain project but not designed

to be adaptable to general project conditions.

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Additionally, the approach considers only the 10 dominant issues observed in the underlying

case study. Consequently, the approach presented by Werner and AbouRizk [2015] does not

holistically address the influence of disturbances on tunneling project performance.

2.3 Research goals

Conventional tools for planning of tunneling construction are valuable for approximations

and first estimations, but they have several drawbacks when applied to detailed investigations.

These drawbacks can be addressed by process simulation. The previous section compared five

relevant approaches. In brief, all of the compared approaches have very interesting aspects and

address specific aspects in a valuable manner. However, from the author’s point of view, limited

transparency is the major drawback in installing process simulation as a planning tool in the

construction industry. For civil engineers without programming or simulation expertise, it is

basically impossible to validate the implementation. Additionally, the available approaches are

mostly focused on supporting management decision and thus have a low level of detail. While

the reduced complexity facilitates the application, neglecting the interdependencies resulting

from coupled processes may cause serious downtimes and should be addressed. Since

disturbances might occur in the production and logistic processes, the associated

interdependencies must be modeled at a detailed level. Otherwise, the generated results would

not be detailed enough to support decisions concerning possible alternatives. However, the

explicit simulation of disturbance events is mandatory for ensuring robust planning and

designing a setup suitable for the project’s boundary conditions. Finally, it is vital to ensure a

low modeling effort in order to provide acceptance by the industry. The requirement for a

simple, graphical model representation, as stated by AbouRizk et al. [1992], must be considered.

In order to develop an approach to overcome the discussed drawbacks, the following

research goals can be identified:

1. Transparency to facilitate assessment by users with limited simulation experience

2. Consideration of uncertainties resulting from operational data and geotechnical conditions

3. Consideration of interdependencies between production and logistic

4. Consideration of disturbances and their influence on dependent and successive processes

5. Limited effort of model development and easy system parameterization to ensure acceptance

A simulation-based approach that addresses these aspects will support the planning of

tunneling projects to minimize avoidable downtimes through adequate setups and will consider

unavoidable downtimes.

2.4 Methodology

The following chapter provides details of the methodology applied by the author. An

approach based on process simulation is presented to quantify the influence of operational

disturbances on the production performance of mechanized tunneling projects. This requires a

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thorough system analysis to identify relevant elements, behavior, and interdependencies. To

ensure transparency and comprehensibility, a standardized modeling technique is applied.

Uncertainties are assessed and modeled accordingly. The formalized model is implemented in a

simulated environment. The implementation is based on distinct simulation modules. The

componential character facilitates model development and supports reusability. The reduced

modeling effort enables project manager to analyze various supply chain setups in terms of their

sensitivity towards disturbances. In combination with uncertain input data, realistic and sound

results are gathered. Thus, project managers are given a transparent basis to plan resource

allocations and determine a robust project planning.

Figure 3 gives details about how the postulated research goals are addressed in the

developed approach. The concept is based on the procedure model for simulation studies by

Rabe et al. [2008b] and depicts the single steps taken for development. Background information

of the applied methods can be found in Chapter 3.

Figure 3: Methodical concept of the presented approach

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First, the research goals are stated clearly. The second research goal, i.e. consideration of

uncertainties, is not listed in the figure explicitly as it is a fundamental aspect of simulation.

Requirements are then derived from the stated goals. The desired transparency is addressed by

choosing a graphical modeling technique for both the formal and simulation models. Graphical

modeling is quite similar to flowchart modeling, which is a very popular graphical description

method in engineering. The graphical and formal description of the model enables engineers to

understand and verify the presented simulation-based approach, even with limited expertise in

simulation.

Quantifying the influence of disturbances on production and identifying logistic bottlenecks

require the holistic modeling of production and logistic processes, as well as the consideration of

interdependencies in the supply chain. How these requirements are addressed is described in

the next passage.

The last stated goal relates to user-friendly implementation with limited effort required for

model development and possibly an easy graphical user interface to ensure acceptance in the

industry, as suggested by AbouRizk et al. [1992]. From this goal, the requirements of reusability,

flexibility, and extendibility are derived and addressed by implementing the approach in distinct

simulation components that can be assembled flexibly to a simulation model. The simulation

components can be used in the simulation framework by dragging and dropping on a canvas.

This provides a high level of reusability and thus significantly reduces the effort needed for

model development. Furthermore, a central service to ensure communication between

simulation components is implemented in the simulation framework. This ensures the flexible

arrangement of simulation components and allows other researcher to extend the presented

approach easily.

After the definition of requirements, the work continues in a two-tracked manner. The actual

development of the simulation-based approach is displayed on the left track, while the gathering

of adequate data is illustrated on the right track.

Abstracting the real-world system in a standardized manner leads to a formal model

description. The scope of this system analysis is determined by the stated goals and defines the

level of detail required to achieve them. The approach at hand is formalized in the System

Modeling Language (SysML) notation. SysML is a standardized graphical modeling notation,

about which more information is provided in Chapter 3. SysML was designed to address the

complexity in describing physical systems and transferring this information to software

implementation. Thus, it is most suitable for the presented challenges.

The system formalization in particular addresses structure (elements), behavior (processes),

and interdependencies. The hierarchical composition, decomposed by the particular elements,

provides the basic structure of the model. All the processed elements either feature intrinsic

processes or present a pre-condition to execute processes. Elements that are unrelated to any

processes are not considered. The system’s behavior is thus described by the processes and

their interactions (e.g. conditions and results). Standardizing the abstraction allows for easier

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communication and documentation of the developed model and is generally believed to be a

crucial step in system engineering. Additionally, the verification process (comparing software

implementation and conceptual model) can be facilitated by a convenient formalization.

The condensed and structured information of the formal model is then used to implement the

simulation model. The presented approach is developed in the commercial simulation

framework AnyLogic [XJ Technologies, 2015]. The implementation is described in detail in

Chapter 7. The simulation model strongly resembles the formal model, which facilitates the

validation process. It is based on distinct simulation components to ensure flexibility, easy

model adaption, and reusability. Additionally, the simulation model remains extendable and

future requirements can be addressed easily.

The second track of work is the acquisition of input data to conduct simulation studies. A

simulation study strongly relies on the quality of available information for generating valuable

results. Thus, gathering and processing project data is a crucial task. This includes, on the one

hand, information about the project itself (e.g. length of tunnel, country, dimensions of jobsite

etc.). On the other hand, operational data must be gathered. This includes process durations,

time between failures (TBF), or average material consumption. Typically, this data has

uncertainties and should be expressed in a suitable manner. The presented approach makes use

of the probability distributions derived through distribution fitting (see Chapter 6).

Finally, a specific scenario can be analyzed and possible alternatives can be evaluated. A

simulation model can be developed by application of necessary simulation components to model

the prevailing situation. The definition of the project parameters and corresponding input data

enables the user to conduct simulation studies. In order to achieve proper results, the Monte-

Carlo experiment should be carried out. Based on the simulation studies, possible bottlenecks or

critical elements can be identified.

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3 Background

A brief introduction to the applied methods is given in this chapter. The presented thesis

applies three major methodologies: simulation, formal model description, and addressing

uncertainty. Each is discussed separately. At first, a brief introduction into the subject with

references to fundamental literature is given. This is followed by a survey highlighting academic

efforts and applications in the construction industry or relevant adjacent fields.

3.1 Simulation

This chapter provides a brief instruction to simulation in general and applications in the

construction industry in particular. The extended length of the chapter is due to its significance

in the presented thesis.

3.1.1 General introduction

In general there are several reasons to analyze a system. The most frequently encountered

reasons are:

Effectiveness (maximizing output while maintaining resource input)

Efficiency (reducing costs while maintaining resource input)

Safety

System dimension to suit project requirements

Identification of possible alternatives

Evaluation of alternatives concerning project requirements

Evaluation of cost-benefit ratio when increasing/reducing resources

Investigation of how a system actually works

This list is by far not exhaustive but it gives a glimpse into the motivation for system analysis.

The methods to carry out such an analysis are displayed in Figure 4. Very often, it is very

expensive or dangerous to vary a real system to investigate one of the reasons stated above. The

system to be evaluated might not exist yet and is just an artificial design. A model-based

investigation can be categorized as a physical or a mathematical representation of the system.

The mathematical models are further distinguished as analytical solutions and simulations.

This thesis focuses on the application of simulation. As Banks et al. [2001, page 3] state in the

very beginning, “[…] simulation is the imitation of the operation of a real-world process or system

over time. Whether done by hand or on a computer, simulation involves the generation of an

artificial history of a system and the observation of that artificial history to draw inferences

concerning the operating characteristics of the real system.” A simulation thus aims to mimic a

real (or hypothetical) system to investigate its behavior. In the course of this thesis, the word

“simulation” always references to a simulation performed by computer.

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Figure 4: Methods of system investigation according to Law [2014]

It can be further distinguished as white (or glass) box simulation and black box simulation. If

all system elements as well as their relations and mechanism are known, a white box simulation

model can be developed to investigate parameter or element variations, for example. Thus, it

allows the simulation of an unknown output through the variation of input. In black box

simulation on the other hand, the modeler has no knowledge of the system components or their

interactions but knows only the relation between input and output. Black box simulation studies

are conducted to understand the system at hand [Bratley et al., 1987].

3.1.2 Procedure model for simulation studies

In order to gather valuable results, simulation models must be verified and validated to

ensure that the defined objectives are met and the simulation study answers the desired

questions. The terms verification and validation are briefly explained by Balci [1998] and Rabe

[2008]. Verification ensures that a model is transformed correctly from one method of

description to another one. It can be simplified as modeling things right. Validation ensures that a

model imitates the (adequately abstracted) behavior of a system accurately. This can be

verbalized as modeling the right things.

In order to support these quality criteria, several procedure models for simulation studies

have been presented over the years, of which two are discussed briefly in this chapter. The first

one was presented by [Banks et al., 2001] and is displayed in Figure 5.

Initially, the problem must be stated clearly. This allows the definition of objectives which

should be met by the study and the general plan to conduct it. In a parallel step, the system is

abstracted in a conceptual model and the relevant data is acquired. These steps are closely

related to the defined objectives. Following this, the model is translated. While it is not stated

explicitly in the schema, this translation results in a computer-executable simulation model. This

simulation model must be verified against the set objectives and validated with the collected

data. An iterative step with both model conceptualization and data collection is mandatory in

case one of this quality gates is not passed. If both are passed, the experimental design stage

begins. Here, important parameters of the simulation experiment are determined, such as length

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of simulation run, warm-up phase, and repetition per simulation run. The term warm-up phase

must be understood as the duration between system initialization and start of the experiment.

When initialized, the system is often blank or empty and crucial dependencies or effects do not

manifest until the system is at “operating temperature.” For example, if the model to be studied

investigates the pedestrian flow of a subway station, the warm-up phase is the duration until a

usual concentration of simulated persons is present in the system. In the presented approach,

the warm-up phase can be neglected since the real system features no such thing and rather

starts with an “empty system.” Furthermore, the length of the simulation run is determined by

the projected length of the tunnel to be investigated. Once these parameters are known,

production runs of the simulation model can be performed. Depending on the analysis of

gathered results, additional simulation runs or an adaption of experiment design settings might

be necessary. If this is not the case, the results can be documented and reported to the client or

management. The last step, implementation, refers to the adaptation or creation of the

investigated system; it must not be confused with implementation into the computer software.

Figure 5: Procedure model for simulation studies according to Banks et al. [2001]

The second one, presented by Rabe et al. [2008b], is the basis for the methodical approach

followed in this thesis. A reference in the English language is given in Rabe et al. [2008a]. This

model is based on a directive from the German Engineers Association [VDI, 2014].

The model is depicted in Figure 6. Ellipses represent steps in the simulation study, while

rectangles represent the intermediate results of these steps. It is clear that verification and

validation receive a higher significance in this approach. Sterman [2000] dedicates a whole

chapter to discuss the point that “modeling is iterative.” This highlights that all intermediate

Verified?

Data collection

Problem formulation

Experimental design

Production runs and analysis

Documentation and reporting

Implementation

Setting of objectives and

overall project plan

Model conceptualization

Model translation

Validated?

More runs?

Verified?No

No No

YesYes

No

Yes

Yes

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results must be validated and verified against (all) previous results, thus ensuring that mistakes

that are identified can be remedied at early stages and the defined goals can be met. It is thus

recommended to check each intermediate result for these quality indicators continuously

throughout the simulation study. Nevertheless, the main elements of the two procedure models

are similar.

Figure 6: Procedure model for simulation studies according to Rabe et al. [2008b]

The task description is derived from the sponsor’s needs (problem statement). The workflow

then splits in two parallel streams. Based on the study objectives, project data is collected.

Usually, this project data must be processed to be used into a simulation model. This step results

in prepared (input) data. The model is developed in parallel to data acquisition. The initial step

here is a thorough system analysis which results in a conceptual model. So far, the approach is

similar to the one developed by [Banks et al., 2001]. But at this point an additional step is

proposed: the formalization of the system. This formal model aims to facilitate the verification

between an informal conceptual model and the corresponding implementation into an

V&

V o

f d

ata

an

d m

od

els

Task defintion

Data collection

Model formalisation

Implementation

Experiment and anylsis

System analysis

Data preparation

Sponsor needs

Task description

Conceptual model

Raw data

Formal model

Prepared data

Executeable model

Simulation results

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executable simulation model. It is not necessary to apply a standardized formalization

technique. The representation can be based on an individual set of elements. However, there are

numerous methods and it is advisable to support the communication between participating

project members. The implementation of an executable simulation model is thus preferably

based on the formal model. Finally, the simulation model is used for experimentation. The

gathered results are analyzed, documented and presented to the client. The approach is

developed to support simulation experts in their work and thus starts with the evaluation of the

client’s needs and ends with the gathered simulation results. There is no stage implementation

at the end.

Among simulation experts it is agreed, that verification and validation are a tedious and

difficult task at best and sometimes even impossible due to system complexity or unknown

dependencies. A provoking but interesting quote by [Box and Draper, 1987, page 424] states:

“Essentially, all models are wrong, but some are useful.” This short quote puts the main

requirement when working with models straight to the point. All models are developed to

investigate a certain aspect or characteristic of a system. Therefore, models can only be

validated or verified in regards of the goals defined.

The approach at hand is based on the procedure model presented by [Rabe et al., 2008b]. The

intermediate models and results were checked repeatedly concerning correct transformation.

The verification proved to be an iterative task. The validation of the presented approach is

addressed in Chapter 8.

3.1.3 Simulation concepts

The previous chapter introduced to the general procedure to develop a simulation. This

chapter presents some concepts for the actual simulation implementation.

[Law, 2014] classifies systems to be either discrete or continuous. While he acknowledges that

any real system is seldom purely discrete or continuous, he argues that usually one

characteristic dominates the other and classifies the system accordingly. Discrete-event

simulation is suitable to simulate discrete systems. System dynamics is a concept applied to

simulate continuous systems. In the following, a brief description for these two simulation

concepts is given. Other simulation methods, e.g. agent-based simulation, Monte-Carlo-Method, or

numerical solver to differential equations are not described at this point.

3.1.3.1 Discrete-event simulation

Discrete-event simulation (DES) is a simulation concept for process and event centered

applications to model sequential systems. This simulation concept has its beginnings around

1960 with a wide range of contributions and different approaches of programming. A very

informative summary of the DES history can be found in [Nance, 1993].

The basic idea of DES is that a system can be expressed by a set of states and their transition

over time. The transitions are instantaneously (discrete) and referred to as events, hence the

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name. A state transition may affect the condition of the whole system. The number of states and

events is finite and can be modeled explicitly. Events occur if a certain condition is met. This can

be for example a specific amount of resources available or a certain amount of time has elapsed.

There are several key elements of a DES model. The most prominent are the system state and

the event. [Banks et al., 2001] also lists system, model, entity, attributes, list, event notice, event

list, activity, delay, and clock. [Law, 2014] provides a more implementation centric view and lists

system state, simulation clock, event list, statistical counters, initialization routine, timing

routine, event routine, library routine, report generator and main program.

A short example is used to describe the main elements. The exemplary system is a single

counter in a bank. Customers arrive in the bank and wait in line until they are served by a teller.

The teller (or server) has two basic states: idle or busy. If there are no customers at the bank, the

server is idle. If a customer (entity) enters the bank (system) and the server is idle, the entity can

immediately proceed to the server. The server’s state is thus changed to busy. Therefore, the

arrival of a customer is an event because it may change the state of a server. If the server is

already occupied with another entity, the arriving entity is queued in line. Once an entity is

served, it leaves the system. If there are no further entities waiting in the queue, the server’s

state changes back to idle. Thus, the second event is the departure of the entity.

Figure 7 depicts the occurrence of events over time. At t0, the first entity enters the system.

The second customer enters at t1. The time between the arrivals is labeled A1 and is randomly

distributed. The third customer arrives at t3, and so on. The first entity can proceed directly to

the server. At t2, this first entity is served and leaves the system. The service time is labeled S1.

The first customer waiting in the queue can now proceed to the teller and is served at t5.

Figure 7: Representation of event occurrence over time according to Law [2014]

The example described above provides an entity-centric view of the system, where an entity

(customer) flows through the system and is served at one or several station(s). This view is very

useful for simulation models of manufacturing or logistic network systems, where the entities

represent materials and (pre-) products that are processed at stations and allocate resources

accordingly. Such simulation studies usually aim to identify a setup with efficient resource

allocation through the reduction of cycle time and queue length. This way of modeling a system

is called the process-interaction approach. Another way of looking at things is the next-event-

scheduling approach, where, the focus is on the occurrence of events. The time between these are

clearly stated and can thus be scheduled. A third approach that must be mentioned at this point

t0 t1 t2 t3 t4 t5

S1

A1 A2 A3

S2

time

[45]

is the three-phase approach (formerly known as activity-scanning). Here, each time the

simulation clock advances, the conditions of the processes are checked to see whether they can

be executed or not. This approach is very suitable for scheduling problems in the construction

industry where the technological dependencies between predecessor and successors are

combined with various requirements concerning resources, durations, space limitation, and

other conditions [Banks et al., 2001].

3.1.3.2 Continuous simulation

System dynamics is a concept used to simulate systems in which a continuous change of

states is observed over time. Unlike in DES, the state transitions are not instantaneous but are

continuous over time. The dependencies are thus usually expressed by differential equations

expressing time-based changes of elements, including related interdependencies. For a system

with sophisticated equations in combination with complex system structures like feedback

loops, analytical solutions are not feasible and simulation must be applied. System dynamics has

a very simple working principle and yet is powerful in its capacity to model complexity. It is

mostly applied to the designing or investigation of policies for political issues or planning

strategies for economical or military eventualities [Law, 2014]. Predator-prey models are also

very common, but they can generally be applied to simulate any system characterized by a

continuous change in the state variables. Probably the most prominent application is the world

model presented by Meadows [1972]. Its objective is to investigate the limits of the world’s

economic growth. The simulation model for this study was developed by Forrester [1973], from

whom system dynamics originated in the 1950s. The model considers population,

industrialization, food production, and resource exploitation and their effects on economic

growth.

An example is used to explain the notation, which features three main elements: stocks, flows,

and causal influences. Stocks are represented by rectangles, flows by pipes, and influences by

arrows. Additional elements include parameters, conditions, flow rates, variables, and system

boundaries. The example in Figure 8 describes the change in the world population in a highly

simplified manner. The population is modeled as a stock, which can be seen as a pool or

accumulation of resources. Stocks increase or decrease over time through flows. The

population’s state is affected over time through births and deaths. The birth rate and death rate

determine the extent of the change, e.g. 1000 births per year. The clouds represent boundaries

of (sub-) systems, which can be used to break up complex structures [Law, 2014].

[46]

Figure 8: Simplified system dynamic model of the world population

3.2 Conceptual model description

In Section 3.1.2, two procedure models to conduct simulation studies are presented. The

conceptual model of the system is a crucial part of this procedure. This chapter presents

standardized model description frameworks with a special focus on the applied SysML. The

chapter closes with a review of the approaches applying SysML to support simulation studies.


The preliminary analysis of the system aims to describe all relevant elements, structures,

behavior, and interactions. Especially, in complex and extensive systems, this model generates

knowledge about particularities. Additionally, it is a crucial element for the verification of

developed simulation models. The repetitive comparison of implementation in the simulation

framework and initially conceptualized model ensures a targeted and especially correct activity.

The scope and level of detail in a conceptual model description depend on the initially stated

objectives. The definition of boundaries and the corresponding system input and output are also

subject to these objectives. Sources of the required system information are documentation,

investigations, and visits (if possible), and especially interviews with experts.

Since the scope depends on the stated objectives, the specific requirements of the system

description are somewhat different. For example, one study aims to evaluate the efficiency of a

production line while another focuses on the position, orientation, and capacity of escape routes

in the same factory. The system factory is basically the same, but the scope of investigation is

different. Consequently, a modeler faces the problem of identifying a modeling technique that

suits his needs.

A distinction can be made between formal or informal and textual or graphical modeling

techniques. A written description without any ontology is the basic of an informal, textual

description of a system. An example of an informal, graphical description method is a self-

defined flowchart. While international standardization is available [ISO, 1985], the application

frequently results in adaptions from this standard. Flowcharts are typically applied to depict

algorithm or complex processes. A formal, textual description method is, for example, the object

constraint language (OCL) [OMG, 2014]. The OCL defines seven keywords that are used to

describe constraints within a class diagram from the unified modeling language (UML) notation

[OMG, 2015b]. Weilkiens [2007] is of the opinion that UML will become the common standard

for modeling. UML is a graphical language especially designed for software development.

briths

brith rate

population

deaths

death rate

[47]

However, several other formal, graphical modeling paradigms are also very popular. The

business process modeling notation (BPMN), also released by the Object Management Group

(OMG) [OMG, 2013], is very popular for the modeling, evaluation, and documentation of

business systems. The Integrated Definition (IDEF) modeling method was initially developed by

the US Airforce as a free standard [Marca and McGowan, 2006]. The IEEE Standard [IEEE, 1998]

gives several subclasses of IDEF for modeling specific areas, such as functional modeling or

information modeling.

The graphical modeling languages described above provide a varying range of assets to

describe the structural and/or behavioral aspects of systems in a conceptual manner. There are

several other modeling languages that are not listed explicitly at this point. Additional details

and other modeling techniques can be found in the works of Weilkiens [2007] and Hommes

[2004].

3.2.1 System Modeling Language

The previous section presented common techniques for conceptual modeling. The decision

about which one to apply requires a profound understanding of the description paradigm, the

system and the requirements for modeling respectively. It should be stated at this point that it is

not mandatory to apply a standardized framework. A self-defined description method is

sometimes encountered. However, a standardization format is advisable in terms of reusability

and exchangeability. In the proposed approach, two basic requirements lead to the decision to

apply SysML. The studied system is characterized by construction elements and interacting

machines. Thus, a hardware-oriented description method is desirable. On the other hand, the

presented approach is simulation-based. Thus, the conceptual model is implemented in a

simulation framework and used for verification purposes. Consequently, the modeling technique

should also be software-sensitive to facilitate the transformation and review.

SysML provides a perfect mixture of these of these two requirements. It is a subset of the

UML standard [OMG, 2015a]. UML was developed to support the modeling of software systems.

It is generally possible to model hardware or mechanical systems in UML, but some aspects are

difficult to represent [Holt, 2004]. The specification of SysML was initialized to support the

requirement definition for system engineering [OMG, 2015a]. System engineering is a discipline

that enhances existing or designed new systems. This includes modeling of facilities, procedures,

data, or mechanical units, as well as software or hardware. A very good introduction to the field

of system engineering is given in Walden et al. [2015] and Eisner [2008].

As a general purpose modeling language designed for system engineering, SysML is used to

describe the structural and behavioral aspects of a wide range of systems. The structural

composition can be classified as relationships that can be visualized easily. The system’s

behavior can be modeled as function-based, message-based, or state-based. Specification of

physical constraints and parameters is also supported. Furthermore, requirements, test cases,

and design guideline can be stated. But most importantly, all of the above mentioned aspects of a

[48]

system can be associated with each other. This provides a holistic model-view of the system and

allows various fields of application [Friedenthal et al., 2011].

SysML provides the possibility to create a holistic view of various kinds of systems in a

software-sensitive manner. Therefore, it is considered most suitable for the development of

conceptual models in simulation studies. Several contributions apply SysML as a formal model

description for simulation-based analysis. The two-part contributions given by Peak et al. [2007]

and Friedenthal et al. [2007] actively demonstrate by application examples the use of SysML in

supporting simulation-based design in the system engineering process.

Schönherr and Rose [2011] and [2009] present a general meta-modeling approach to

automatically transfer the software independent SysML description to executable simulation

models in a specific framework. Their approach focuses on manufacturing systems and is thus

not applicable for the approach presented in this thesis. However, the author supervised a

Master’s thesis [Westphal, 2013 and Westphal and Rahm, 2013] which successfully transferred a

general SysML model representation into an executable AnyLogic model. Within this thesis, a

structural system description and the corresponding description of the element’s behavior are

transferred by means of triple graph grammar concepts. This automatic model transformation

was successful but many unresolved issues remain. The author of this thesis has decided to focus

on the general idea of the concept and the transformation concept was not followed up.

However, the possibility to transfer a general SysML into an executable simulation model

clearly demonstrates the resemblance between the two methodologies. Consequently, the

methodologies to develop the presented approach are chosen well.

The language is based on nine diagrams that represent these aspects concerning behavior,

structure, and requirements. Figure 9 depicts this classification including the specific diagrams.

SysML is a subset of the UML standard and reuses several of the UML diagrams. Out of the nine

SysML diagrams, three are modified from the UML notation, four are identical with the UML

notation, and only two were especially developed for the SysML notation [OMG, 2015a].

The requirement diagram (req) was newly introduced to clearly state requirements and

design guidelines defined for the system. The activity diagram (act) is a modified UML diagram.

It is used to model the logic of how an action is executed in regards of input, output and control

algorithms. It also defines the transformation of input or output induced by the action. The state

machine diagram (stm) is a standard UML diagram to model the processes performed by an

entity. The execution of a process can be subject to the occurrence of an event and/or produce

an event. The exchange of events, messages, or information between the system’s elements can

be modeled with sequence diagrams (sd). The fourth behavioral diagram of SysML is the use

case diagram (uc), which is also directly reused from UML. It is used to express how a system or

parts of the system interact(s) with external stakeholders and states their objectives for using

the system.

The main diagram of structural modeling with SysML is the block definition diagram (bdd). It

is a modification of the class diagram in UML. All relevant elements and their properties are

[49]

modeled as blocks. The representation of composition, generalization, hierarchy, and

relationship is possible. A SysML representation of the UML composite structure diagram is

provided by the internal block definition diagram (ibd). It is modified to depict the connections,

interfaces, and interdependencies between physical elements in a better manner. The

parametric diagram (par) is the second plain SysML diagram. It is applied to state the

constraints and formulae for engineering analyses. The package diagram (pkg), also a standard

UML diagram, provides the possibility to depict the model organization by clustering model

elements for certain views.

Figure 9: SysML taxonomy according to OMG [2015a]

The presented approach applies only four of the nine SysML diagrams: block definition

diagram, internal block diagram, state machine diagram, and sequence diagram. This summary

is intentionally short and general. SysML is a comprehensive and complex modeling technique

and the official specification [OMG, 2015a] is rather abstract. The author recommends the

following literature for a detailed introduction to SysML, and especially its application in model-

based system engineering: Weilkiens [2007], Holt and Perry [2008], Friedenthal et al. [2011],

and Delligatti [2014].

SySML Diagrams

Behavior StructureRequirment Diagram

(req)

Activity Diagram (act)

Sequence Diagram(sd)

State Machine Diagram(stm)

Use Case Diagram(uc)

Block Definition Diagram (bdd)

Internal Block Diagram(ibd)

Parametric Diagram(par)

Package Diagram(pkg)

Standard UML diagram (abbreviation)

Modified UML diagram (abbreviation)

SysML diagram (abbreviation)

[50]

3.3 Input data for simulation models

The following chapter provides information on the collection and preparation of data to be

used in simulation studies. The first section of this chapter describes the methods of data

acquisition and how these observations can be used for modeling uncertainty. The presented

approach is based on the use of probability distributions in the Monte-Carlo experiments of a

simulation model. Therefore, Chapter 6 presents information about how such functions can be

derived from observations. The generation of random values during simulation experiments is

described hereafter. The chapter closes with an overview of approaches focused on uncertainty

modeling in the construction industry.


This chapter focuses on the struggles to acquire adequate input data for simulation studies. A

simulation model is usually designed to serve a certain purpose. This can be, for example, the

evaluation of alternatives, planning of a production line, or the maximized throughput of an

existing system by efficient resource allocation. The goals of the study define the purpose and

focus of the simulation model. The same is true for the input data. The scope defines the times,

rates, probabilities, and so forth which must be measured. The quality of the input data has a

crucial effect on the quality and usefulness of the experiments results [Robertson and Pepera,

2001]. However, high-quality input data is often difficult to gather, especially since construction

processes have certain unique conditions. Measurements from completed projects can often not

be transferred due to different situations. Compared to the stationary industry, construction

processes are executed in smaller numbers, which additionally reduces sample sizes. A smaller

sample set implies a lower quality. Moreover, the (partial) system might not even exist yet and

thus measurements are simply impossible. There are more reasons that the collection and

preparation of input data is such a tedious and difficult task. It is well known among simulation

experts that the quality of any simulation model strongly depends on the quality of the input

data. The modeling itself is usually rather straight-forward, but the collection and preparation of

input data can prove very difficult.

In general, there are four basic methods to gather input data for simulation studies:

Automatic surveillance (sensors, cameras, microphones, etc.)

Manual measurements and documentation

Parameters published by manufacturers

Assumptions

The collection of input data by automatic surveillance is the method of choice. The data can

be acquired in digital format and in large amounts. The resolution and measuring error of

sensors is known and can be considered. Algorithms support can be trained to identify events in

video or audio sequences. The relevant data can thus be gathered with minimum human

resource allocation. However, this method is not feasible for all situations. Additionally, the

necessary equipment can be very expensive.

[51]

Measurements done by hand are a common method to collect information about existing

systems. This method is very flexible and simple to perform but also tedious and prone to errors.

Nevertheless, it is probably the most common method.

When standardized equipment is used in the simulation model, the parameters published by

the manufacturer provide a third option of input data. For example, the technical data sheet for

cranes provides the velocity of every movement that the crane can perform. This information

can be applied in a model to simulate the unloading process of material deliveries. The technical

data sheets usually state the maximum of a parameter as a single number. For example, the cat’s

velocity can be given as 45 m/min. This parameter can be used in a simulation model. However,

in the real system, the cat will be moved carefully to control the load’s pendulum movement.

Even if the cat is moved at top speed, acceleration must be considered. Therefore, measuring the

duration of un-/loading sequences would be preferable.

If the previously described methods to derive input data are not possible, assumptions must

be made. Educated guessing should be the last resort, but sometimes it is the only choice. It is

advisable to perform several experiments with a selected range to assess the system’s sensibility

regarding assumption. This facilitates the decision as to whether an optimistic, neutral, or

pessimistic assumption should be used.

3.3.2 Addressing uncertainty in input modeling

Generally, there are three ways to use the collected measurements in a simulation model.

First, trace-driven simulation experiments use the collected measurements directly as input

values. However, this approach only reproduces historical observations and is not really suitable

for large numbers of simulation runs as needed in Monte-Carlo experiments. The second

approach to use measurements in a simulation study is the application of empirical distributions

derived from the sample set. For continuous distributions, this approach remedies the limited

number of samples experienced in trace-driven simulations. The third option, generally referred

to as distribution fitting, is argued to be the most desirable of the three [Cheng and Cheng, 1994;

Law, 2014]. Here the parameters of theoretical distributions are adjusted to represent the

collected data. The theoretical distribution neglects possible irregularities that are often

encountered when deriving empirical distributions from small sample sets. Furthermore,

theoretical distributions enable sampling outside the measured limits, if this is desired. In some

cases, however, this is physically or logically impossible and the distribution must be truncated

to avoid such errors.

Several other approaches have been presented to address uncertainty in input data. These

include for example delta-method approaches, Bayesian methods averaging, (nonparametric)

bootstrap re-sampling, and induced distribution methods. A discussion of these methods can be

found in Henderson [2003] and Barton [2012]. A general downside of these methods is their

complexity and the problem that fundamental assumptions are seldom justified in practice

[Law, 2014].

[52]

The presented approach applies theoretical distributions to collected measurements. This

method is well-established in simulation applications and is sufficient to demonstrate the

developed concept.

[53]

4 Formalization of the mechanized tunneling domain

A fundamental aspect of the presented thesis is the thorough analysis of the structural and

behavioral aspects of mechanized tunneling. This system analysis is the foundation of all further

developments. In order to ensure the reusability of the description and facilitate validation of

the developed approach, the well-established SysML standard is applied. SysML is a graphical

notation standard developed especially for general-purpose modeling in systems engineering. In

this thesis, it is applied to describe the mechanized tunneling system in a formal but transparent

manner. The names of structural elements, processes, interactions, or any other elements that

appear in the presented SysML diagrams are italicized to highlight them as model elements.

4.1 Specification of the system analysis scope

The first step in system analysis is the definition of relevant system elements, system

boundaries, and influences of or on the environment. This system definition is bound to the

desired outcome of the investigation. In the context of this thesis, the research goal can be

generalized as an investigation to provide holistic performance planning and evaluate

production disturbances. Thus, the investigation focuses on production and logistic processes of

mechanized tunneling. To specify the system, Figure 10 presents a context diagram which is a

modified internal block diagram of the SysML notation to describe the relevant domain of

mechanized tunneling.

The relevant system, demarked as the<<domain>> mechanized tunneling domain, is

decomposed into four elements, as discusses in Section 1.1.2. The sub-elements TBM and backup

system are marked with the label <<system >>, indicating that they are in fact distinctive

technical systems. The other two sub-elements, tunnel and surface facility, cannot be addressed

in the same way and are thus labeled as <<domain>>, with a less strict intention. The superior

domain mechanized tunneling interacts with two <<external>> domains, both classified as

environment. The subterranean conditions of the environment include the geotechnical conditions

of the encountered subsoil and the constraints resulting from existing development at the

surface. The geotechnical conditions influence the design of the applied TBM. The existing

development is classified as part of the subterranean environment because the interaction with

the mechanized tunneling domain takes place here in the form of heaving or settlement. The

specific threshold of displacement is connected to several structural factors, and also to the type

and importance of the building, e.g. historical church or governmental department.

The second <<external>> domain represents the surface. The location of the construction site

strongly influences the project. Metro projects are often located in urban areas, where space is

typically scarce. A ban on night shifts is also possible in residential areas. Furthermore, the

location influences the quality of infrastructure and the traffic density. This in turn relates to the

supply of the construction site with consumables and materials. The external logistics is a crucial

[54]

part of the supply chain. However, it is not within the scope of the approach presented in this

thesis and thus classified as an <<external>> element.

Figure 10: Context diagram of the mechanized tunneling domain

4.2 Structural decomposition

The structural system decomposition aims to identify relevant elements and derive their

parameters. The block definition diagram of the SysML notation is uses to show the relation of

the elements with regard to the system’s hierarchy. Consequently, this step investigates the next

hierarchical level of the mechanized tunneling domain. The elements discussed in this section are

sequenced as follows: TBM, backup system, tunnel, and surface facilities. Differences between

Slurry shield machines and EPB shield machines are highlighted and discussed separately.

Obviously, the point of view defines the level of detail and perspective of the model. For

example, if the desired result is the wear analysis of the cutting tools, than the model should

contain details concerning the abrasiveness of the encountered soil, as well as information about

the number, type, and spacing of the mounted tools, etc. Hence, in the context of the presented

system analysis, only those aspects that are relevant for emulation of production and logistic

processes are modeled. In the following, the system decomposition is described by SysML block

definition diagram, including relevant aspects concerning the defined analysis scope.

<< Context Diagram>>ibd Mechanized Tunneling Domain

«external»subterraneanCondition : Environment

«external»existDevelopment

: Location

«external»geotechnicalCond

: Environment

«external»externalLogistic

: SupplyChain

«external»constructionSite

: Location

«external»surfaceCondition : Environment

«domain»MT : MechanizedTunneling

«domain»tunnel

: Tunnel

«system»TBM

: TunnelBoringMachine

«system»backup

: Backup System

«domain»jobsite

: SurfaceFacility

: ext1 : ext2

[55]

4.2.1 Tunnel boring machine

The production cycle of a TBM is characterized by the sequential alternation of advance and

ringbuild. There are some elements found in both slurry shield machines and EBP shield

machines. The common elements of the <<system>> TBM are: cutting wheel, excavation chamber,

backfill grouting system, erector, and thrust cylinder. Their structure is depicted in Figure 11. The

most prominent difference between slurry and EPB shield machines is the method of face

stabilization. But the specifics of this process are not within the scope of this thesis and thus not

modeled. In the defined scope, the major difference is the method of mucking. In slurry shield

machines, muck is dissolved in a watery bentonite suspension, which is then extracted by a

slurry pump. EPBs on the other hand are usually used for soils with a high share of fine particles.

This pasty muck can be extracted by a screw conveyor from the excavation chamber. These two

mucking elements are generalized in mucking device TBM, as the system strictly requires the one

or the other.

Figure 11: SysML bdd showing the general components of TBM

«block»Excavation Chamber

properties

diameter : Lengthlength : Lengthcur volume : Volumetotal volume : Volume

flow specification

inout muck : Muck in bentonite : Bentoniteout slurry : Slurry

«block»Mucking Device TBM

properties

cur extraction rate : Ratemax rate : Rate

«block»Cutting Wheel

properties

diameter : Lengthexcavation rate : VolFlow

flow specification

inout muck : Muck

«block»Slurry Pump

«block»Screw Conveyor

properties

cur extraction rate : VolFlowmax rate : VolFlow

flow specification

inout slurry : Slurry

flow specification

inout muck : Muck

«block»Erector

properties

dur segment installation : Time

flow specification

inout segment : Segment

«block»Thrust Cylinders

properties

number : Integercur stroke : Lengthstroke length : Lengthcur advance rate : Velocitymax advance rate : Velocity

«block»Backfill Grouting System

flow specification

in hardener : Hardener inout inout grout : Grout

properties

cur injection rate : VolFlowmax rate : VolFlow

bdd Hierarchical decomposition [General assembly structure of TBM]

«system»

TBM

[56]

The flow of materials is modeled separately in Section 4.5. The diagram shows the materials

that enter and/or leave an element. For example, the flow specifications of cutting wheel indicate

that muck goes in and out of the element, because the excavated soil passes this element before it

enters the excavation chamber.

As indicated in the beginning, the level of detail is according to the requirements. For cutting

wheel, only two properties are relevant in the given context: the diameter and the excavation

rate. The diameter defines the area of excavation. The value of this property is of the type length.

The diameter is relevant for calculating the volumetric flow rate of muck that enters the

mechanized tunneling domain. This flow rate is labeled as excavation rate and calculated by the

multiplication of diameter and advance rate (specified in the thrust cylinder). The excavation rate

is of the type VolFlow (short for volumetric flow).

The excavation chamber separates the tunnel face from the atmospheric working area. The

support pressure to stabilize the face is established here if required. However, for the defined

purpose, only the geometric properties are of interest. The total volume is important for

assessing the capacity to store muck. Steel installations in the excavation chamber to guarantee

structural stability and extra mixing of the muck are neglected at this point. The current volume

represents the amount of soil that is momentarily present in the excavation chamber. The flow

specifications indicate that muck can flow in and out of the element, as is the case for EPB shield

machines. For Slurry shields, mucking is based on the slurry circuit. Thus, bentonite is pumped

into the excavation chamber, where it is mixed with the entering muck. It is now referred to as

slurry to indicate that it is in fact a mixture.

The backfill grouting system is a series of pumps connected to nozzles at the end of the shield.

Grout is used to backfill the annular gap that remains when the shield advances. This backfilling

is done continuously during advance for reducing settlements and providing a stable bedding of

the lining. Its relevant properties are the current injection rate of grout and the maximum rate

possible. The latter limits the speed of advance if a high quality of grouting is to be preserved.

Usually, a readily mixed grout is transported to the TBM and then injected. Thus, the flow

specifications show an in and out flow of grout. Recent developments aim for the utilization of

two-component grouts, in which the hardening process can be controlled precisely. The second

component is mixed into the grout just moments before injection. It is abstracted as hardener at

this point. The flow specifications state that while hardener might enter the backfill grouting

system, only grout will leave, because the two components are mixed here.

The erector, a robotic arm with up to six degrees of freedom, is used to install the lining

segments. It lifts the segments, e.g. by application of a vacuum, and moves them to the position of

installation. The handling of segments by the erector is modeled accordingly. For ringbuild,

segments are lifted from a corresponding place in the backup system and installed to form

another piece of the tunnel. Concerning the defined research goals, only the duration of segment

installation is relevant and thus depicted here. As opposed to the previous properties, this

duration of segment installation is an abstraction. It is not a purely mechanical aspect but rather

[57]

a combination of the erector’s motion speed and the time needed by the working crew to

temporarily fasten the segment in place. Therefore, this property is abstracted as a period of

time for a single segment installation. This enables the investigation of mechanized tunneling

projects with varying numbers of segments per ring. Obviously, this system element has other

interesting properties, like the maximum lifting capacity. However, such design parameters can

be assumed to be sufficiently dimensioned by TBM manufacturer and are thus not modeled

distinctly in the present approach.

A ring of thrust cylinders pushes the machine forward into the ground whilst using the last

segmental ring as abutment. Their number is coupled to the amount of segments per ring. The

total stroke length is equivalent to the length of a segmental ring. Advance stops when the

current stroke length equals the total stroke length, after which another ring is built. During

ringbuild, the thrust cylinders temporarily secure the segments in place until the ring is finished.

Two additional properties of thrust cylinders are of interest here, the first of which is the current

advance rate, which resembles the momentary speed of progress. Typically, the tunneling

industry relates to this velocity in millimeter per minute (mm/min) as base unit. The current

advance rate determines the duration of the production process advance in terms of the total

stroke length. The TBM manufacturer designs the machine elements for a certain maximum

advance rate, usually in the area of 80–100 mm/min. In practice, it might be possible to exceed

this limitation to a certain extent. However, this might result in quality issues or increased wear

and tear of technical parts. Since quality issues are not considered in this thesis, the maximum

advance rate of thrust cylinders sets a strict limitation to the speed of excavation.

As described earlier, the method of mucking differs between slurry and EPB shield machines.

However, all machines require some kind of mucking system. For this reason, a generalization is

made. Both the slurry pump and the screw conveyor have the same properties. Therefore, they

can be placed in the superior <<block>> mucking device TBM. The current extraction rate defines

the amount removed from the excavation chamber in certain period of time. From the modeling

point of view taken in this thesis, the specific blocks differ solely in their flow specifications. The

slurry pump handles material of the type slurry while the screw conveyor handles muck.

4.2.2 Backup system

The backup system is the part of the supply chain directly attached to the TBM; it is pulled

along on special tracks that are moved regularly during ringbuild. It contains several resources

and auxiliary equipment needed for production. It also provides temporary storage capacity for

consumables and materials. This thesis focuses on the three basic functionalities related to

material-handling: grout-handling, segment-handling, and mucking. These basic functionalities

are essential in every <<system>> backup system as shown in Figure 12. The setup provides

several possibilities for modification. The specifics of the mucking device depend on the type of

machine applied. For Slurry shields, a slurry circuit is mandatory. For EPB shields, mucking can

[58]

be done by a cart-based method or by application of a conveyor. The handling of grout and

segments, on the other hand, allows alternatives for both machine types.

Figure 12: SysML bdd showing the basic functionalities of backup system

Other elements, like hydraulics, electrics, compressed air systems, grease supply, wastewater

handling, operator cabin, and emergency equipment are not modeled in detail due to their

irrelevance to the defined scope. Thus, these three essential functionalities of the backup system

are discussed separately with respect to the defined research goals.

4.2.2.1 Mucking device backup system

The method of muck hauling differs for slurry and EPB shield machines. A generalization of

mucking device of the backup system (BS) is shown in Figure 13. The different methods can be

distinguished as discontinuous or continuous mucking.

Figure 13: SysML bdd showing possible mucking devices of the backup system

The only discontinuous method is cart-based mucking for EPB shield machines. Here, a

backup belt conveyor conveys the muck from the screw conveyor and releases it in the container

of the vehicle. Obviously, advance is only possible if a muck cart is present to take up the muck.

«System»

Backup System

«system»

Grout-Handling «system»

Segment-Handling «system»

Mucking Device

bdd Hierarchical decomposition [General assembly structure of backup system]

disontinuous continuous

«system»Mucking Device

properties

cur extraction rate : VolFlowmax rate : VolFlow

«block»Slurry Circuit BS

«block»Tunnel Belt Conveyor

flow specification

inout bentonite: Bentoniteinout slurry : Slurry

flow specification

inout muck : Muck

«block»Backup Belt Conveyor

flow specification

inout muck : Muck

bdd Hierarchical decomposition [Generalization of mucking device ]

[59]

Continuous mucking includes the application of a belt conveyor for EPB and a slurry circuit for

slurry shield machines. Both allow constant discharge from the TBM without dependency on

available vehicles. The belt conveyor is connected to a tunnel belt conveyor which also transports

the muck through the tunnel. The properties of the specific options are the same and thus

generalized in mucking device. They state the current and the maximum possible volume flow.

Only the flow specifications differ. The slurry circuit can handle slurry and bentonite. This

differentiation allows the modeling of the feeding and removing line of the slurry circuit, as

detailed in the mucking device of the tunnel. The other two elements are only applicable in EPB

shield machines and thus limited to muck transport.

4.2.2.2 Grout-handling device backup system

Figure 14 shows the SysML block definition diagram of the grout-handling of the backup

system that supplies the backfill grouting system on the TBM. At this point, a principal

differentiation between the more common one-component and the innovative two-component

grout is needed. If the one-component grout is applied, two system elements are required. The

grout pump transfers material from the delivering vehicle into the grout tank. Since there is no

need to operate this element slower than the maximum rate, current flow rate is omitted at this

point. The grout tank is characterized by the volume currently stored and the maximum volume

possible.

Figure 14: SysML bdd showing possible grout-handling devices of the backup system

1-comp grouting 2-comp grouting

«system»

Grout Handling

flow specification

inout grout : Grout

«block»Grout Line- Comp A

properties

cur transfer rate :VolFlowmax rate : VolFlow

«block»Tank - Comp B

properties

cur volume : Volumetotal volume : Volume

«block»Grout Pump

properties

max rate : VolFlow

«block»Grout Tank

properties

cur volume : Volumetotal volume : Volume

flow specification

inout hardener : Hardener

bdd Hierarchical decomposition [Generalization of grout handling ]

[60]

In recent times, the application of two-component grout has been increasing. Here, a very

slow-hardening grout (Component A) is constantly pumped through pipes from the surface

facility to the backup system. The grout line, Component A, is characterized by the current transfer

rate and a maximum rate. Just before injection into the annular gap, the slow-hardening

component A is mixed with a hardener (Component B). This additive, e.g. water glass, initiates

the hardening process. This allows precise regulation of the hardening reaction. Component B is

delivered by vehicle and stored in the tank Component B, which is characterized by the current

volume and the initial maximum volume. The superior block grout-handling backup system

features a flow specification for grout. Thus, every derived element also features this very flow

specification. The tank component B additionally features a flow specification for hardener.

4.2.2.3 Segment-handling Device backup system

The segment-handling on the backup system is required to unload segments from the vehicle

and transfer them to the pick-up place of the erector. A differentiation can be made with regard

to the possibilities of temporarily storing a segmental ring. Figure 15 shows a SysML block

definition diagram of the segment-handling device with two possible specifications: without

segment feeder and with segment feeder. Selecting the method to be applied depends on the

space available but also on economic issues and customers preferences.

Figure 15: SysML bdd showing possible segment-handling devices of the backup system

A common system element in both types is the segment crane, which lifts the segments from

the delivering vehicle one at a time. This element is characterized by the speed of an unloading

sequence and the maximum lifting capacity, labeled as weight. If a segment-handling device

without segment feeder is used, the segment crane releases the segment on a segment table. This

«system»

Segment Handling

flow specification


without feeder

«block»Segment Crane

properties

velocicty : Velocitylifting capacity : Weight

with feeder

«block»Segment Transfer Table

properties

currently stocked : Boolean

«block»Segment Feeder

properties

cur stock : Integermax stock : Integer

bdd Hierarchical decomposition [Generalization of segment handling ]

[61]

is the pick-up position for the erector. Obviously, this pick-up position has the limited storage

capacity of a single segment. The abstracted property currently stocked is of the type Boolean and

indicates whether or not the element can take another segment from the segment crane. As soon

as the erector removes a segment, the crane can release another one. Thus, the unloading

sequence strongly depends on the duration of segment installation. Consequently, if ringbuild

takes very long, the vehicle must wait accordingly until all segments are unloaded.

In order to uncouple the unloading sequence of the vehicle from the duration of ringbuild, a

segment feeder can be applied. The segment crane is again part of this setup, but the lifted

segments are dropped on the segment feeder instead. This element resembles a conveyor for

segments with the usual maximum stocking capacity of a complete ring. Each time the crane

releases a segment, the feeder moves it one position further. Only the last position of the feeder

can be reached by the segment crane. The first position is the pick-up place of the erector. This

way, the feeder serves as a buffer to constantly provide segments for the production process

ringbuild. This features two major advantages: the delivering vehicle is not blocked by the

duration of the production process and segments are available when required.

4.2.3 Tunnel

The tunnel is the final result of the construction. During project execution, however, it is also

part of the supply chain of mechanized tunneling. It directly connects the backup system and the

surface facilities. The current project status defines the length of the tunnel and thus the

dimension of this supply chain part. However, tunnel does not qualify as a system itself and is

thus given the weaker label <<domain>>, as can be seen in Figure 16.

The tunnel itself has two relevant properties, the current length, which can be calculated by

the length of one segment multiplied by the number of rings already installed, and the projected

length at the end of the tunneling project. As indicated earlier, the tunnel is actually a succession

of distinct rings that can in turn be decomposed into distinct segments. Each ring features a

unique number. The number of segments is usually defined by the diameter, which can be

distinguished as inner diameter or outer diameter. The ring is assembled by the installation of

several segments, each of which has a distinct type. In order to provide traceability and quality

management, every segment has a unique ID. The remaining properties of the segment define its

physical appearance: length, width, thickness, and weight. It should be noted that length and

width are often differently interpreted, because segments are installed orthogonal to the tunnel

axis. In the context of this publication, the length of a segment is measured in the same direction

as the length of the tunnel.

If the vehicle is a train, tracks are required in the tunnel. These tracks are usually only

installed temporarily during project execution and removed after competition. Therefore, the

track is only associated with tunnel. The affix, is laid in, emphasizes the association of the two

elements. Track features a single relevant property: the extension length, which defines the

distance for which the project can advance before the track must be extended.

[62]

Especially in long tunneling projects, the dependency on the delivering vehicle becomes very

influential. In order to allow the passing of delivering vehicles, switches should be installed at a

reasonable distance. Switch is associated with a specific track. A switch is defined by the specific

type, e.g. California switch, and its location in the tunnel. The location is differentiated as the

beginning or the end of the switch. The stereotype of both properties is a floating-point number

(i.e. type real), to allow floating-point numbers to define an exact position. One switch is usually

dimensioned for one vehicle (total capacity). The property currently occupied provides

information about whether or not a vehicle is momentarily parked in the switch. It can thus be

modeled by the type Boolean. This piece of information is crucial for deciding whether or not a

vehicle can enter the next track passage between two switches. The system element tunnel

pipeline is abstracted in Figure 16. A detailed definition can be found in Appendix A.

Figure 16: SysML bdd for the tunnel domain

4.2.4 Surface facility

The surface facilities resemble the fourth element relevant to the presented system analysis

of the mechanized tunneling domain. Like tunnel, they are given the label <<domain>> (see Figure

17). Their relevant properties are length and width, i.e. the actual geometric size.

An essential component is the shaft that provides access to the tunnel at a plane level. It is

thus allocated to tunnel. If more than one TBM has to be supplied from the same surface facility,

either a large shaft provides access to all tunnel entrances or several shafts provide individual

access. Therefore, an unambiguous ID is required for allocation. Shaft is characterized by the

geometric features length, width, and depth. The possibility of circular shaft geometry is

abstracted. Furthermore, the shaft provides loading positions for vehicle(s). The number of

loading positions is modeled as parking capacity of the type integer. The property vehicle parked

«block»Ring

properties

number : Integernumber of segments : Integerinner diameter : Lengthouter diameter : Length

flow specification

in segment : Segment

«block»Track

properties

extension length : Length

bdd Hierarchical decomposition[General structure of tunnel domain]

«domain»

Tunnel

«block»Switch

properties

position start : Realposition end: Realtotal capcity : Vehiclecur occupied : Boolean

properties

cur length : Lengthprojected length : Length

«block»Segment

properties

id : IDtpye : Stringlength : Lengthwidth : Lengththickness : Lengthweigth: Wheigth

«block»Tunnel Pipeline

is laid in

allows passing of vehicles on

is attached to

[63]

indicates the specific instance when vehicle is currently present in the shaft area. The flow

specifications show that only vehicles enter or leave the block shaft.

The other two components of the surface facilities are cranes and storage for the various

materials and consumables. In the context of this thesis, only the materials directly related to the

production processes are considered. Other materials like grease, oils, pipes etc. are not

modeled. The surface facility includes segment storage, grout batching plant, and auxiliary

storage. Additionally, a temporary muck pit is modeled. Storage areas for the various materials

are detailed separately in Appendix A.

As opposed to high-rise construction sites, the layout and space allocation of tunneling

projects remain constant throughout the implementation phase. The location of storage areas

and installations remains unchanged during the progress. Thus, the location and operation

range of cranes can remain constant throughout the project. Therefore, stationary gantry cranes

are frequently used for material-handling at the surface facilities in mechanized tunneling

projects. Other cranes like mobile cranes or rotating tower cranes can also be applied. From a

process-oriented point of view, the material transfer is similar for all of them. Therefore, the

presented approach abstracts them to gantry cranes as a commonly applied device. When

several gantry cranes are used, they are distinguished by their ID. In combination with more

than one shaft, an unambiguous allocation is thus achieved. Gantry cranes move on special

tracks. The position of these tracks is specified by their beginning and end and thus defines the

range of the crane. The current position must be modeled also. The lifting capacity defines the

maximum weight that can be moved. Obviously, this must be designed carefully beforehand. The

remaining properties of gantry crane concern the speed of movement. The speed of the actual

portal is labeled as velocity beam. Velocity cat specifies the horizontal movement along the beam.

The vertical movement of the load is modeled by velocity hook. Figure 17 also depicts the

material flows associated with gantry crane. Segments are picked up from the corresponding

storage and released to the vehicle. The handling of all other materials, especially liquids, is

assumed to be done by container (see Appendix A). For example, if cart-based mucking is

applied, the gantry crane removes the muck container from the vehicle and empties them into a

muck pit. Then the empty container is placed on the cart again.

Generally, trains are used as transporting vehicles. The tire-based MSV is an alternative that

runs directly on the tunnel floor. Thus, no track, and especially no regular track extension, is

required, which can make up for the higher initial costs. The properties and flow specifications

of a train and MSV are similar. A distinction is thus unnecessary and the more general term

vehicle used. Vehicle is not a component of the surface facilities as such; it is merely allocated to

shaft and tunnel. The specific vehicle is identified by an unambiguous ID. Since a speed limit for

loaded vehicles is common, the traveling velocity is distinguished as loaded or unloaded.

Furthermore, the loading capacity is of interest. The total segment capacity defines whether or

not a complete segmental ring can be transported at once. The property segment stock specifies

the segment that is currently loaded. Additionally, the vehicle provides space for container. A

[64]

distinction is made between the general container capacity and the specific container stock. Only

the element itself is specified at this point but not the content of a container. Consequently, the

two flow specifications of vehicle are the segment and the container.

For Slurry shield machines, a soil treatment plant is necessary for processing the extracted

slurry. The reusable bentonite suspension is separated from the fine-grained muck. After this

treatment, bentonite is recirculated into the slurry circuit and muck released into the muck pit

until removal. The current inflow of slurry can be derived from the removing line of the slurry

circuit. The outflow of the soil treatment plant includes outflow of muck and bentonite.

Consequently, the flow specifications of the soil treatment plant specify that while slurry goes in

the element, either muck or bentonite goes out. Furthermore, the location of the soil treatment

plant is stated.

Figure 17: SysML bdd for the surface facility domain

Additional modeling information for storage, transported cargo, and liquids is given in

Appendix A.

allocated to

properties

locationX : ReallocationY : Realcur inflow slurry : VolFlow max in slurry : VolFlow cur outflow muck : VolFlow max out muck : VolFlow cur outflow muck : VolFlow max out muck : VolFlow

flow specification

in slurry : Slurry out muck : Muckout bentonite : Bentonite

«block»Soil Treatment Plant

«block»Shaft

properties

id : IDlocationX : ReallocationY : Reallength : Lengthwidth: Lengthdepth : Lengthparking capacity : Integer vehicle parked : Vehicle

flow specification

inout vehicle : Vehicle

«domain»

Surface Facility

«block»Vehicle

properties

id : IDvelocity unloaded : Velocityvelocity loaded: Velocityseg capacity : Integerseg stock : Segmentcontainer capacity : Integercontainer stock: Container

flow specification

inout segment : Segmentinout container : Container

«block»Gantry Crane

properties

id : IDrange X Start : Realrange X End : Realrange Y Start : Realrange Y End : Realcur position X : Realcur position Y : Reallifting capacity : Weightvelocity beam: Velocityvelocity cat : Velocityvelocity hook: Velocity

flow specification

inout segment : Segmentinout container : Container

properties

length : Lengthwidth : Length

«block»Storage

allocated to

if Slurry

bdd Hierarchical decomposition [General structure of domain surface facility ]

«domain»

Tunnel

allocated to

allocated to

[65]

4.3 Composition of three sample projects

The previous chapter highlighted the possibilities of alternative project setups. In this section,

three example setups are presented. The decision about whether slurry or EPB shield machines

are applied is usually made according to the encountered geotechnical conditions. However,

their field of operation overlaps in a certain range [Maidl et al., 2012]. Assuming that both

machine types are generally possible, mucking can be performed by one of three alternatives. It

can be done continuously through a slurry circuit or a tunnel belt conveyor or intermittently by

means of muck carts. Grouting can be done with one or two components. Finally, the handling of

segments in the backup system is possible with or without application of a segment feeder.

These alternatives can generate a total of 18 different project implementation strategies only if

the decision between the slurry and EPB shield machine does not depend on the prevailing soil

conditions. If the soil conditions influence the preliminary decision for a specific machine type,

the alternatives for grout and segment-handling remain. Thus, slurry shield machines allow for

six and EPB shield machines for 12 different project compositions.

As an example, Figure 18 presents the composition of a slurry shield machine. The general

decomposition of the mechanized tunneling domain (see Figure 10) is instantly visible.

Figure 18: SysML bdd showing the decomposition of a slurry shield machine setup

bdd Hierarchical decomposition [Slurry shield machine with feeder and 1-component grouting]

<<domain>>

Mechanized Tunneling

<<system>>Slurry TBM

<<system>>Backup System

<<domain>>Tunnel

<<block>>Cutting Wheel

<<block>>Erector

<<block>>Slurry Pump

<<block>>Backfill Grouting

System

<<block>>Thrust Cylinder

<<block>>Excavation Chamber

<<domain>>Surface Facility

<<block>>Shaft

<<block>>Grout Batching

Plant

<<block>>Segment Storage

<<block>>Gantry Crane

<<block>>Soil Treatment

Plant

<<block>>Muck Pit

<<block>>Vehicle

<<block>>Grout Tank

<<block>>Grout Pump

<<block>>Segment Crane

<<block>>Segment Feeder

<<block>>Ring

<<block>>Segment

<<block>>Slurry Circuit BS

<<block>>Track

<<block>>Switch

<<block>>Slurry Circuit BS

[66]

In a slurry shield machine model, the central elements are slurry pump (component of TBM),

slurry circuit (backup system), slurry pipes (tunnel), and soil treatment plant (surface facility).

Additionally, the project is modeled by application of a segment feeder and one-component

grouting.

A second example illustrates the differences in mucking between slurry and EPB shield

machines. Figure 19 shows the composition of an EPB shield machine with cart-based mucking.

Typical for EPB shield machines is the use of screw conveyor as the mucking device of TBM.

Additionally, a backup belt conveyor is modeled in the backup system to release the muck into

the carts waiting below. Thus, the vehicle requires a mucking capacity, which, however, is not

displayed explicitly. The muck carts are emptied by the crane (surface facility) directly into the

muck pit. Therefore, the soil treatment plant of slurry shields is not necessary anymore. Apart

from tracks and switches, the tunnel domain requires no additional installations such as slurry

pipes.

Figure 19: SysML bdd showing the decomposition of cart-based mucking for EPB shields

A major disadvantage of cart-based mucking is related to the limited muck capacity of

vehicles. For large diameters, the required disposal capacity usually exceeds the capacity of a

single vehicle. As soon as one vehicle is full the excavation must be stopped until another

mucking vehicle is provided. This interdependency between the production process advance

bdd Hierarchical decomposition [EPB shield machine with cart-based mucking, 1-C-grouting – no feeder]

<<domain>>


<<system>>EPB TBM


<<domain>>Tunnel


<<block>>Erector



System




<<block>>Shaft


Plant



<<block>>Muck Pit

<<block>>Vehicle

<<block>>Grout Tank

<<block>>Grout Pump


<<block>>Segment Table

<<block>>Ring

<<block>>Segment

<<block>>Backup Belt

Conveyor

<<block>>Track

<<block>>Switch

[67]

and the supply chain easily results in performance losses. Furthermore, the segment-handling in

the backup system provides no temporally storage. Instead of a segment feeder, only a simple

segment table is installed. Thus, the vehicle must wait until ringbuild is finished before all

segments are unloaded. The setup for grout-handling is the same as Figure 18. However, grout is

also delivered by the vehicle, which is already affected by segment-handling and mucking.

Consequently, this setup exhibits strong interdependencies between both production processes

and the supply chain because all materials are handled solely by the vehicle.

Figure 20 depicts an EPB shield machine where muck is removed by a conveyor to overcome

the disadvantages of the previous setup.

Figure 20: SysML bdd showing the decomposition of belt-based mucking for EPB shields

This method of continuous mucking resolves the earlier problem of interdependency

between advance and available muck-carts. The mucking capacity of the vehicle is not required.

Instead, a backup belt conveyor is installed in the backup system. It is connected to a tunnel belt

conveyor that directly hauls the muck from the backup system to the muck pit. The tunnel belt

must be extended regularly, resulting in a major downtime. Furthermore, a two-component

grout is used. The slow-hardening grout is pumped directly through pipes and thus independent

bdd Hierarchical decomposition [EPB shield machine with belt-based mucking, 2-C-grouting and feeder]

<<domain>>


<<system>>EPB TBM


<<domain>>Tunnel


<<block>>Erector



System

<<block>>Tank – Comp B

<<block>>Grout Comp A Feeding Line



<<block>>Ring

<<block>>Segment

<<block>>Backup Belt

Conveyor

<<block>>Grout Line

<<block>>Tunnel Belt

Conveyor




<<block>>Shaft


Plant



<<block>>Muck Pit

<<block>>Vehicle

<<block>>Track

<<block>>Switch

[68]

of vehicle deliveries. The additional hardening agent is usually quite economical and lasts for

several advance steps. Thus, the need to suspend excavation due to lack of grout is also reduced.

4.4 Material-handling operations related to vehicle

The vehicle plays a central role in the various material-handling operations in mechanized

tunneling. Depending on the setup, it might transport all the materials involved. The context of

these operations is presented in the SysML internal block diagram given in Figure 21.

The logical starting point of the description is the loading of materials. The vehicle awaits

loading by the gantry crane in the shaft area. The crane removes material from the

corresponding storage (i.e. segments, grout, and/or hardener). The vehicle enters the tunnel on

tracks which begin at the shaft and stretch to the current position of the backup system. Switches

in the track allow the passing of multiple vehicles traveling on the same track. Once the vehicle

arrives at the backup system, various material-handling operations are performed by separate

system elements. Segmental lining is unloaded piecewise by the segment crane. If one-

component grouting is applied, a grout pump unloads the grout. In the case of two-component

grouting, the vehicle delivers the hardener instead of grout. This hardener is unloaded by an

auxiliary crane. The material-handling at the backup system might include more than just

unloading operations. If mucking is performed by carts, then the backup belt conveyor loads

muck onto the vehicle. After all the required logistic processes are performed, the vehicle

returns to the surface facility where the muck-carts are unloaded by a gantry crane, if necessary.

Hereupon, another logistic cycle could restart.

Figure 21: SysML ibd showing the vehicle’s relations to material-handling operations

<<ContextDiagram>>ibd Specification of vehicle in material handling context

<<block>>Grout Pump


<<block>>Track

<<block>>Switch

<<block>>Shaft


<<block>>Vehicle

<<block>>Storage

<<block>>Muck Pit

empties muck

loads / unloads

waits at

waits at

picks material

unloads grout

unloads segments

loads muck

starts at

drives on

allows passing of

installed in

ends at <<block>>Backup System

accesses

<<block>>Tunnel Belt Conveyor

<<block>>Aux Crane

unloads hardener

[69]

The context diagram depicts the interaction between system elements during material-

handling operations performed by vehicles. Figure 22 provides the SysML internal block

diagram depicting the various materials possibly transported. The vehicle commutes between

the surface facility and the backup system. The surface facility provides storage for segments,

grout, and hardener. By means of the gantry crane, all these materials are loaded onto the vehicle.

The excavated muck is removed by the gantry crane and transferred into the muck pit. The figure

clearly shows that all material-handling operations in the surface facility are performed by the

gantry crane. The exchange with external domain is not shown in this diagram.

Figure 22: SysML ibd showing materials transported by vehicle

Material-handling in the backup system is executed by a distinct system element, as opposed

to the surface domain. Each material type is handled by a corresponding element. The

interaction with system boundaries is not modeled explicitly at this point. The diagram

highlights the two basic bottlenecks of the supply chain in mechanized tunneling. The gantry

crane is used for material-handling operations at the surface facility. Trucks delivering segments,

grout, and hardener are also unloaded by the crane. Only the removal of muck from the muck pit

is performed by diggers. The second bottleneck of the logistic chain is the vehicle itself. If

material-handling operations are disturbed or capacities are insufficiently planned, the result is

a production standstill.

ibd Material flow [Specification of materials transported by vehicle]

<<block>>Grout Tank


<<block>>Vehicle


<<block>>Muck Pit



<<block>>Temp Storage

Muck

<<block>>Grout Batching Plant

<<block>>Auxiliary Storage

SegmentGrout Hardener

Muck

HardenerGrout

Segment <<block>>Gantry Crane


<<block>>Grout Pump


<<block>>Backup Belt Conveyor

<<block>>Aux Crane

<<block>>Backup Belt Conveyor

[70]

This diagram clearly depicts the importance of the sound planning of the logistic system. The

dependency between production processes and logistic processes is obvious. If the system is not

dimensioned sufficiently, a loss of performance can be expected.

4.5 Specification of material flows

In order to model dependencies between production and supply chain, the flow of materials

must be modeled in detail. The specific material flows are depicted in SysML internal block

diagrams. The system elements, modeled in Section 4.2 as <<block>>, are stations of material

transportation. Ports in the blocks indicate the direction of the material flow. Flows between

blocks are labelled to specify the material flow. A port in the diagram frame signifies that a

material is either transported beyond or delivered from system boundaries.

4.5.1 Grout

Grout is essential to backfill the annular gap during excavation. There are numerous possible

elements that define the grout’s properties, like hardening time, water resistance, process

ability, general strength, and so on. The mixture can be adapted to the encountered conditions.

What is relevant for this system analysis is not the specific mixture but how grout is transported.

Generally, two possibilities must be analyzed, namely one-component and two-component

grouting. In this context, the number of components does not refer to the elements but to the

method of injection.

The commonly applied method is one-component grouting, where a ready-to-use mixture is

injected. Figure 23 shows the SysML internal block diagram of the material flow of one-

component grouting.

Figure 23: SysML ibd the showing material flow for one-component grouting

The delivery of grout by external logistics is abstracted as an inflow from <<external>> surface

domain. It is stored in a grout batching plant. The notation of the blocks differs from the previous

groutPlant: Grout Batching Plant

container1 : Cargo

gPump : Grout Pump

gTank : Grout Tank

bgs : Backfill Grouting System

ibd Material flow [Flow for one-component grouting]

GroutGrout

Grout

Grout Grout

<<external>> Subterranean Domain

<<external>> Surface Domain

Grout

crane1 : Gantry Crane

train1 : Vehicle

surface handling by transport through tunnel by

[71]

section. The label groutPlant represents an instance of the type grout batching plant. The grout is

forwarded to container1. All cargo elements are handled by a crane at the surface. All cargo units

are transported by a vehicle (i.e. train1). When the train arrives at the backup system, the grout is

unloaded by a pump and stored in a tank. Finally, the backfill grouting system forwards the grout

out of the system. The supplementary information concerning cargo items (i.e. container1) is

shown exemplarily only in this diagram. The various interactions between material flow and

vehicle are holistically depicted in Figure 22. The corresponding diagram for the application of

two-component grouting is given in Appendix B.

4.5.2 Segmental lining

The tunnel lining in soft grounds is typically constructed by assembling several segments into

a ring. The timely transport of these segments is an essential task of the supply chain. If

segments are not available for ringbuild, the construction is at a standstill. The presented system

analysis aims to model the reasons for downtime in mechanized tunnel construction and

includes the alternatives to handle the segmental lining.

The structural analysis presented in the previous chapter provides two alternatives for

segment-handling in the backup system: with or without segment feeder. In Figure 24, a setup,

including a segment feeder, is described. Material-handling with a segment transfer table is

depicted in Appendix B.

The lining segments are stored in the segment storage on the surface. A gantry crane removes

them from the storage. Depending on the size of the segments and the crane, the removal is done

either piecewise or batchwise. Either way, the segments are loaded on the vehicle, which

transports them through the tunnel. Upon arrival, the segment crane removes them. This is done

piecewise. The crane places one element at a time at the last position of the segment feeder. They

are then forwarded to the first position where the erector can reach them. The erector picks up

one segment at a time and assembles the ring. In the presented model, the segments thus “enter”

the ring, where they remain.

Figure 24: SysML ibd showing the segment transport for a setup with a segment feeder

sStorage : Segment Storage

crane1: Gantry Crane

train1: Vehicle

sCrane : Segment Crane

feeder: Segment Feeder

ibd Material flow [Flow of segments with segment feeder]

SegmentSegment

Segment

Segment

erector : Erector

Segment

r : Ring

Segment


Segment

[72]

4.5.3 Muck

Another essential part of the supply chain of mechanized tunneling in soft grounds is related

to the removal of excavated muck. The specifics of material transportation depend on the type of

machine applied. In this thesis, three general setups for mucking are evaluated and modeled

accordingly. The mucking with slurry shield machines is based on the slurry circuit. The SysML

internal block diagram depicting the various material flows in a slurry circuit is given in Figure

25.

The bentonite, used to dissolve the soil, enters the system to the bentonite storage. It is

pumped by a slurry pump (i.e. p1) through the feeding line of the slurry circuit until it ultimately

enters the excavation chamber. Here, it is mixed with the muck excavated by the cutting wheel.

The origin of the muck inflow is abstracted by the subterranean domain. Muck and bentonite are

mixed in the excavation chamber and leave it as slurry. Another slurry pump (i.e. p2) extracts the

slurry and propels it through a removing line into the soil treatment plant. The need for

additional pumps to sustain a sufficient flow over long distances is abstracted in this system

analysis. In the soil treatment plant, the bentonite is segregated again and recycled into the

bentonite storage. The remaining muck is released into a muck pit where it is stored until

removal from the facility. This removal is abstracted as an outflow of the system.

Figure 25: SysML ibd showing mucking in slurry shields

Mucking with an EBP shield machine can be done by two setups. The first is using a tunnel

belt conveyor to provide a continuous extraction similar to a slurry circuit. Figur e 26 shows this

extraction. Muck enters from the subterranean domain as it is excavated by the cutting wheel and

enters the excavation chamber. A screw conveyor removes the muck from the chamber and

releases it onto the belt conveyor. The conveyor transports it to the surface facility, where it is

ibd Material flow [Flow of muck in slurry shield machines]

p1 : Slurry Pump

chamber : Excavation Chamber

wheel : Cutting Wheel

removingLine:Slurry Pipe

feedingLine: Slurry Pipe

p2 : Slurry Pump

separation : Soil Treatment Plant

Pit : Muck Pit

bStorage: Bentonite Storage



Muck

Muck Muck

Muck

Slurry Slurry Slurry

BentoniteBentoniteBentonite


Bentonite

[73]

finally disposed into the muck pit. The removal from the muck pit is again abstracted as outflow

into the subterranean domain.

Figure 26: SysML ibd showing belt-based mucking in EPB shields

The alternative setup for mucking in EPB shield machines is given by the still more common

cart-based mucking. This is a discontinous mucking mehtod. Here, the muck is transported

thorugh the tunnel by a vehicle. The material flow is shown in Figure 27. In this setup, a backup

belt conveyor is installed after the screw conveyor. This conveyor only spans from the screw

conveyor to a waiting position for the vehicle. Here, the muck is released into the muck-carts

positioned below. The vehicle hauls the muck to the shaft area. The unloading requires a gantry

crane, which heaves the cargo items from the vehicles and empties them into the muck pit.

Figure 27: SysML ibd showing cart-based mucking in EPB shields

4.6 Process specification of system elements

This chapter provides the details of the processes performed by the previously described

system elements. The SysML state machine diagram is used to express the processes in a formal

manner. Processes are modeled as rectangles with rounded edges. The arrows (transitions)

ibd Material flow [Flow of muck in EPB shield machines with tunnel belt conveyor]



bConveyor: Tunnel Belt Conveyor

sConveyor: Screw Conveyor

pit : Muck Pit

MuckMuck

MuckMuck

Muck



Muck

ibd Material flow [Cart-based mucking for EPB shield machines]



sConveyor: Screw Conveyor

pit : Muck Pit

Muck

Muck Muck

MuckMuck

dropOff: Backup Belt Conveyor

muckCart: Cargo

Muck



Muck

crane1 : Gantry Crane

surface handling by

[74]

between the states specify the order of execution. The transition from one state in another is

triggered if either all specified requirements and/or conditions are fulfilled or a specific duration

has elapsed. Events performed by other system elements can also trigger a transition. The dark-

grey elements depict signals exchanged between elements. The reception of a specific signal can

trigger a transition. The origin and destination of the signals are not specified in a state machine

diagram. These interactions are depicted separately in Section 4.7.

In summary, processes usually depend on other elements (i.e. predecessors) but often

influence other elements themselves (i.e. successors). If preconditions are not fulfilled, the

element cannot operate. Thus, the element is operable only if all predecessors are functioning

and conditions are fulfilled. Consequently, a general distinction between a state of operable and

another state of inoperable must be made where due. This differentiation comprises only of the

dependencies of material, capacity, or other elements. Technical failures that influence the

operability as well are introduced in Chapter 5.

The discussion of state machine diagrams is similar in order to the previous section. First, the

system elements of the TBM are described, followed by backup system and the surface facility.

Several system elements show no process behavior and are thus not specified here. This

includes all elements assigned to the <<domain>> tunnel and additionally excavation chamber,

slurry circuit, grout tank, tank –comp b, segment table, temporary batch storage, shaft, soil

treatment plant, and all elements abstracted as storage, cargo, or liquid.

4.6.1 Cutting wheel

The processes executed by the cutting wheel are shown in Figure 28. The state machine is

initialized through the entry point connected to the state idle. This is the initial state for all the

following state machines. Upon reception of the signal RbFin (ringbuild finished), several

guarding conditions are evaluated. If even one of the guarding conditions is not met, the

execution of the transition is suspended until all the elements required for advance are operable.

Then, the transition from idle to advance is triggered and the signal AdvStart is emitted.

Excavation is finished when the stroke length is reached (signal StrokeComp). Hereupon, the

cutting wheel resumes the state idle and signals the end of advance (AdvFin).

Figure 28: SysML stm of cutting wheel

idle

AdvStartRbFin excavation

AdvFin

stm Process description [Processes executed by cutting wheel]

StrokeComp

[grouting operable]AND

[thrust cylinder operable]AND

[mucking device operable]

[75]

4.6.1 Erector

The state machine diagram for erector is provided in Figure 29. If advance is finished, i.e. the

signal AdvFin is received, and the element thrust cylinders is operable, then ringbuild can start. A

corresponding signal (RbStart) is sent to other system elements. Hereupon, the compound state

ringbuild begins. The first internal state entered is pickup, which models the movement of the

erector to the temporal storage place for the segments, e.g. the last position of a segment feeder

or the segment table. When the corresponding segment-handling device signals the availability of

a segment (SegAvail), the erector removes this very segment, which it confirms by emitting

SegRemove. Consequently, the state machine remains in pickUp until a segment becomes

disposable. The transition leading away from the state install is triggered after a specific

duration has elapsed. This is depicted by the stylized hourglass. The internal process cycle is

repeated until the ring is completed (i.e. all segments installed). Then the compound state

ringbuild is exited, the signal RbFin emitted, and the initial state idle resumed.

Figure 29: SysML stm of erector

4.6.2 Thrust cylinder

Figure 30 displays the state machine diagram for thrust cylinder. The thrust cylinders are

relevant to both the production processes in mechanized tunneling. In advance, they push the

machine forward. In ringbuild, they keep the single segments in place until the ring is complete.

Therefore, the initial state idle is exited if either ringbuild (RbStart) or advance (AdvStart)

commences and the state active is entered. With respect to the defined research goals, a detailed

specification of the performed activities is irrelevant and therefore not modeled. The trigger for

the transition from active to idle depends on the currently executed production process. If

ringbuild is performed, the reception of RbFin switches the state machine from active to idle.

During advance, the thrust cylinders determine the end of the production process. When the

length of the current stroke equals the total stroke, the currently driven advance step is complete

and a corresponding signal (StrokeComp) is emitted to notify other elements.

idle

RbStart

AdvFin

pickUp install

SegAvail

ringbuild

RbFin

stm Process description [Processes executed by erector]

[ring complete]

[thrust cylinder operable]

SegRemove

[76]

Figure 30: SysML stm of thrust cylinder

4.6.3 Backfill grouting system

Figure 31 presents the state machine diagram for the backfill grouting system. If grout is not

available, the state machine becomes inoperable until the grout is delivered and handled

accordingly. This is controlled by a guarding condition. The exclamation mark in the condition

represents a false condition and can be read as a NO or NOT. The guarding condition of the input

transition to the state inoperable can thus be read as no grout available. The history state (H*)

ensures that the previously active sub-state is resumed again if grout runs out during

excavation. The transition from idle to the active state grouting is triggered if the required

material is available and the signal AdvStart is received. The state grouting is exited as soon as

the advance is finished (AdvFin). Hereupon, the backfill grouting system resumes the state idle.

This system element is inactive during ringbuild.

Figure 31: SysML stm of backfill grouting system

4.6.4 Mucking device on TBM

The structural aspects of the mucking performed in the TBM context allow a generalization in

modeling. The behavioral aspects however differ and thus require separate modeling for slurry

RbStart

active

AdvStart RbFin

stm Process description [Processes executed by thrust cylinders]

idle StrokeComp

[advance] AND

[stroke length reached]

[ringbuild]

idle

grouting

AdvFin

AdvStart

stm Process description [Processes executed by backfill grouting system]

operable

inoperable

[! grout available]

H*[grout available]

[77]

pump and screw conveyor. The elements depend on the availability of material, storage capacity,

or other elements.

4.6.4.1 Screw conveyor

Figure 32 depicts the state machine diagram of the screw conveyor. When the signal advance

is received, the initial state idle is exited and the state mucking is entered. Upon reception of the

signal AdvFin, the initial state is resumed. In the context of this thesis, it is assumed that mucking

is only performed during advance. However, this process execution is possible only if a mucking

capacity is available. This can be either a running tunnel belt conveyor or the presence of a

muck-cart with capacity. If this is not the case, then the system element is not in working order

and switches into the state inoperable.

Figure 32: SysML stm of screw conveyor

4.6.4.2 Slurry pump

The watery bentonite suspension applied in slurry shield machines must be circulated

constantly to prevent sedimentation. This reduces the carrying capacity for muck and eventually

clogs the pipes. Thus, the bentonite suspension is also circulated during ringbuild. Figure 33

displays the state machine of the slurry pump.

At first, a general distinction is made about whether the system element is operable or not.

The entry point is connected to operable. The availability of a mucking capacity is abstracted to

be the precondition to be fulfilled for proper operation. The guarding condition (muck capacity)

is constantly monitored. If it is triggered, it transforms the state machine from the current state

into the state inoperable. The history state guarantees that the previously active state is

subsequently resumed even if it was inside a compound state.

If the slurry pump is operable, the idle state is left when advance or ringbuild starts. If the

signal AdvStart is received, the state mucking is entered and a corresponding signal (MuckStart)

is emitted. As soon as advance is finished, mucking is exited, the signal MuckFin emitted and idle

state is resumed. If ringbuild starts (signal RbStart), the slurry pump must continue to circulate

idle

AdvStart

mucking

AdvFin

stm Process description [Processes executed by screw conveyor]

operable

inoperable

H*

MuckStart

MuckFin

[! muck capacity]

[muck capacity]

[78]

the bentonite suspension. Consequently, the state by-passing is entered. This compound state is

distinguished as two sub states. If an extension of the pipes is required, the state pipe extension is

entered. A guarding condition triggers the end of this state. If no extension is necessary, the

slurry pump executes a regular by-pass during ringbuild, which ends when ringbuild is finished

(RbFin).

Figure 33: SysML stm of slurry pump

4.6.5 Segment-handling device of backup system

From a behavioral modeling point of view, the segment crane and segment feeder must be

described for the segment-handling device of the backup system. The element segment table acts

as a mere storage and is thus not described.

4.6.5.1 Segment crane

The state machine diagram of the segment crane is shown in Figure 34. The initial state idle is

exited upon reception of the signal DeliveryS. This signal is emitted by the vehicle when it arrives

at the backup system with a new batch of segments. The segment crane signals the beginning of

the unloading process (UnloadSStart) to retain the vehicle in position. Hereupon, the compound

state unloading changes into the state pickUp. If storage capacity is available, the segment can be

released into a temporary storage, i.e. segment table or segment feeder. The state drop-off is

entered. The signal SegCraneRelease is sent after a certain period, as indicated by the hourglass.

idle

AdvStart

mucking

RbStart

by-passing

pipe extensionregular by-pass

AdvFin RbFin

stm Process description [Processes executed by slurry pump]

operable

inoperable

H*

MuckStart

MuckFin

[muck capacity]

[! muck capacity]

[finished]

[extension] [else]

[79]

Subsequently, the condition is evaluated as to whether all segments are unloaded already. If this

is the case, the compound state unloading is exited and UnloadSFin signals the end of the

unloading sequence. Otherwise, the unloading sequence restarts with the pick-up of another

segment. The processes executed by this element are all controlled by the reception of signals or

specific guarding conditions. Distinguishing of its operability is thus not necessary.

Figure 34: SysML stm of segment crane

4.6.5.2 Segment feeder

Figure 35 shows the state machine diagram of the segment feeder. The processes of this

element are controlled by the deposition and removal of segments. It features two states, namely

the initial state idle and the active state move. Move forwards the segments to the next position

on the segment feeder. The transition from idle to move is triggered when a segment is removed

from the first position by the erector (signal SegRemove). Another way this transition is

triggered is when the first position is still empty and the segment crane releases a segment (signal

SegCraneRelease). The erector can only reach the first position of the feeder, but the segment

crane releases the material onto the last position. Consequently, the segments are forwarded

until the first the position is filled. This is controlled by the guarding condition first position

empty. Then the erector can reach a segment for ringbuild. Once the erector removes it, another

movement of the feeder forwards the next segment to the first position. Assuming the feeder is

filled in a gapless manner, each movement provides another segment for pickup. Thus, once the

first position is occupied, the movement is solely controlled by the removal of segments through

the erector.

The process move is finished after a specific duration, as indicated by the stylized hourglass.

When the transition back to the initial state idle is triggered, a condition evaluates the signals to

be sent. If the first position of the segment feeder is full, the signal SegAvail is emitted to the

erector. Either way, the signal StorageCapac is sent, which informs the segment crane that

another segment can be placed on the feeder.

DeliveryS

UnloadSStart

unloading

pickUp

dop-off

UnloadSFin

SegCraneRelease

StorageCapac

idle

stm Process description [Processes executed by segment crane]

[all segments unloaded]

[else]

[80]

Figure 35: SysML stm of segment feeder

4.6.6 Vehicle

The vehicle is involved in several material-handling operations. It delivers consumables from

the surface area to the backup system and can also haul excavated muck. Its process description,

however, is rather simple; the vehicle is either moving or not moving. Nevertheless, due to the

various interdependencies in the loading and unloading processes, the vehicle is blocked in a

certain position until all material-handling operations are finished. Furthermore, it is important

to specify the driving direction and the waiting position.

Figure 36 shows the SysML state machine diagram of the vehicle. It is divided into two

compound states: atSurface and inTunnel. This distinction initiates the appropriate material-

handling operation. The initial state of vehicle is idle in the compound state atSurface. This

corresponds to a waiting position. As soon as an order is available, this initial state is exited. If it

is a delivery order, the vehicle enters the state material-handling surface where it remains until

the signal LoadFin is received from gantry crane. If the received order is a mucking order, the

vehicle directly switches to driving in the compound state inTunnel. Especially in long tunneling

projects, several vehicles are used in order to cope with increasing traveling time. In order to

allow the passing of vehicles, switches must be installed. A basic procedure to model switches is

to investigate if the next section is occupied, and if so, to remain waiting until the next section is

free. However, this procedure neglects the priority of delivering vehicles, which is abstracted

here but implemented in the simulation model. When the vehicle finishes the complete tunnel

length, a condition is evaluated about the destination. The distance is updated after each advance

step. If the vehicle is headed for the backup system, a series of conditions evaluates the signals to

be emitted. In the case of cart-based mucking, the signal MuckingCapac indicates that mucking

can continue. If material is loaded on the vehicle, the corresponding signals (i.e. DeliverS,

DeliverG, and DeliverH) are emitted to trigger the unloading processes. The vehicle then enters

the state materialHandlingBS (backup system), where it remains in wait until all the required

signals are received, whereupon the vehicle returns to driving state to the destination surface.

When it arrives at the surface area, a condition evaluates whether a slot is available or the vehicle

idle

SegRemove

moving

SegCraneRelease

StorageCapac

SegAvail

stm Process description [Processes executed by segment feeder]

[else]

[! first position empty][first position empty]

[81]

has to remain in waiting position. Another condition checks if the muck is loaded. If it is loaded,

the vehicle enters the state unloading and emits the signal DeliveryM to notify the gantry crane

that muck needs to be unloaded. When unloading is finished, the idle state is resumed and the

vehicle awaits new orders. If new orders are already available, the state machine immediately

exits the idle state again.

Figure 36: SysML stm of vehicle

stm Process description [States related to material handling operations related to vehicle]

atSurface

idle

unloading

inTunnel

driving

waiting

MuckingCapac

DeliverySDeliveryGDeliveryH

[mu

ckin

g o

rder

]

[! section free] [destination

Backup System]

waiting

DeliveryM

[slot free]

[slot free]

UnloadM Start

UnloadM Fin

materialHandling Surface

LoadStart LoadFin

[delivery order]

[section free]

materialHandling BS

UnloadH Start

UnloadS Start

UnloadG Start

MuckFin

MuckStart

UnloadG Fin

UnloadH Fin

UnloadS Fin

[tunnel length][destination Surface]

[cart-based mucking]

[hardener loaded] [grout loaded] [segments loaded] [cart-based mucking]

[order available]

[muck loaded][slot blocked]

[82]

4.6.7 Gantry crane

The gantry crane is involved in several material-handling operations. It removes materials

from the various storage areas and loads them onto vehicles parked in the surface area. If cart-

based mucking is used, the gantry crane is also used to empty the muck container into the muck

pit and place it on the vehicles again. The SysML state machine diagram of the gantry crane is

shown in Figure 37. The diagram is based on the assumption that necessary materials and

capacities are available at all times. The initial state idle is exited only if two conditions are met,

that is, a vehicle must be present and an order must be issued. Depending on the type of order, the

gantry crane enters either the loading or the unloadMuckCart state. The compound state loading

starts with the hooking of cargo. The signal LoadStart is emitted to retain the vehicle in the

loading position. The specific state loading is exited after a certain period of time. When the

order is finished, i.e. all cargo items have been loaded onto the vehicle, and no further orders are

pending, the vehicle is released with the signal LoadFin. Otherwise, the sequence is restarted. A

mucking order indicates that the vehicle has muck loaded and needs to be emptied. The

procedure of unloadMuckCart is similar to loading. Again, a signal (i.e. UnloadMStart) is issued to

retain the vehicle. After a certain period of time, the emptying state is exited and a condition

evaluates whether any more muck carts need to emptied. If this is not the case, the signal

UnloadMFin is emitted and the compound state unloadMuckCart is exited. The initial state idle is

resumed again. SysML state machine diagrams for additional elements are given in Appendix C.

Figure 37: SysML stm of gantry crane

stm Process description [States related to material handling performed by gantry crane]

idle

LoadFin

LoadStart

loading

hookingCargo

loading

[else]

DeliveryM

UnloadM Fin

UnloadM Start

hookingMuckCart

emptying

unloadMuckCart

[order finished]

[! further order]

[else]

[else]

[all carts empty]

[mucking order]

[delivery order]

[order available] AND [vehicle at surface]

[83]

4.7 Process interactions

A major aspect of the presented approach is the modeling of process interdependencies and

exchange of information. In the previous section, the described modeling aspects focus on the

intrinsic behavior of the element. In this section, the interaction of elements is depicted with

SysML sequence diagram, of which only two are shown exemplarily. The first introduces the

information exchange involved in advance. The second features the interaction during ringbuild

with a segment feeder involved. The remaining SysML sequence diagrams can be found in

Appendix D.

4.7.1 Interaction of elements during advance

Figure 38 shows the abstracted SysML sequence diagram for advance with slurry shield

machines. The elements are shown in vertical swim lanes. Signals or information exchange

between the elements is depicted by horizontal arrows. The vertical orientation of the diagram

expresses the chronological order of the information exchange.

Figure 38: SysML sd showing the interaction during advance with slurry shields

The initializing signal is RbFin sent from the ENV (environment) to cutting wheel. ENV is a

substitute for an element that is not specified at this point and provides the starting point for all

developed SysML sequence diagrams in this thesis. In the presented case, ENV represents the

element erector, which emits the signal RbFin.

Advance can be started when ringbuild is finished, but only if all components are operable

and the final tunnel length is not yet reached. This is expressed in the stated condition. The

sd Process interaction [Interaction during advance in Slurry shield machines]

ENV :Cutting Wheel :Thurst Cylinder :Slurry Pump:Backfill

Grouting System

RbFin

AdvStart

AdvStart

AdvStart

[all components operable AND tunnellength != complete]opt

StrokeComp

AdvFin

AdvFin

AdvFin

AdvFin

[84]

optional (opt) frame defines the signals that depend on this condition. If the condition is met, the

cutting wheel emits the signal AdvStart, which is received by thrust cylinder, slurry pump and

backfill grouting system. As described in the corresponding state machine, the element thrust

cylinders emits the signal StrokeComp that indicates the end of advance. However, the signal

AdvFin is transmitted again by the cutting wheel.

Taking a process modeling point of view, the difference between advance with slurry shield

machines and EPB shield machines lies in the exchange of the deployed element for muck

removal. In this case, the element slurry pump is replaced with screw conveyor.

4.7.1 Interaction of elements during ringbuild

The second interaction diagram described at this point features the production process

ringbuild (see Figure 39). The abstracted element ENV inserts the message AdvFin. In this case,

the signal is emitted by cutting wheel as a result of the previous sequence diagram. The element

erector receives the signal and the interaction described in the optional frame is executed if the

defined condition is met. The signal RbStart is emitted to various elements. The slurry pump

needs to receive the signal as the element switches to bypass mode during ringbuild. The loop

frame is not exited until the condition (ring complete) is met. Consequently, the segment feeder

and erector exchange signals multiple times before the information exchange is continued. Once

the ring is completely assembled, the signal RbFin is emitted by the erector to the various

elements and to the abstract element ENV.

Figure 39: SysML sd showing the interaction during ringbuild

ENV :Erector :Thurst Cylinder :Slurry Pump : Segment Feeder

AdvFin

RbStart

RbStart

[all components operable]opt

RbFin

SegAvail

SegRemove

[ring complete]

RbFin

RbFin

loop

sd Process interaction [Interaction during ringbuild in Slurry shields with segment feeder]

[85]

5 Modeling disturbances

The main aspect of the presented approach is the quantification of production disturbances

in mechanized tunneling. This chapter separately introduces the modeling of disturbances in the

presented approach. In order to quantify downtimes, the actual reasons and dependencies must

be analyzed in detail. Basically, three different patterns are distinguished by which disturbances

affect the production of mechanized tunneling. These disturbances are categorized as follows:

1. Disturbances directly affecting production

2. Disturbances related to the supply chain

3. Cascading disturbances

This chapter discusses these three patterns separately in the given order and provides details

on how to model them adequately through application of SysML.

5.1 Disturbances directly affecting production

The production cycle in mechanized tunneling is characterized by the sequential alternation

of advance and ringbuild. These abstract processes are performed by the coordinated

interaction of several elements. The breakdown of one of these elements implies the immediate

suspension of production. Thus, several elements are crucial for performing the production

process. For example if a failure occurs in the element erector, ringbuild cannot be continued.

Another example is the suspension of advance, which is advised if the backfill grouting system is

not operating properly. If the correct bedding of the segmental ring is not provided, it might lead

to quality issues with the lining and uncontrolled settlements. The thrust cylinders might also

cause an abrupt disruption. For the excavation process with slurry shield machines, a

functioning slurry circuit is mandatory.

These briefly addressed disturbances typically arise due to the technical malfunction of the

described elements. Therefore, the technical functionality of the system elements must be

distinguished. Some of the developed SysML state machines already feature a distinction of

operability, e.g. the backfill grouting system. The corresponding SysML state machine features a

state inoperable, which is entered if grout is not available. This reason of inoperability is related

to the availability of a resource, i.e. grout, for consumption. To account for technical disturbances

of the element itself, the state inoperable must be enhanced to identify the actual reason of

disturbance. Figure 40 exemplifies the enhanced SysML state machine of the element backfill

grouting system. The exchange of signals is not displayed in the presented figure for two reasons.

First, they are discussed in Chapter 4 where they were originally introduced. Second, the

disturbances are not communicated by signals but triggered by conditions.

When the Boolean variable functioning is set to false, the corresponding transition is

triggered and the compound state inoperable is entered. The actual triggering of disturbances is

[86]

described in the implementation part (see Chapter 7). Within this approach, the triggering of

technical disturbances is controlled by randomly calculated durations.

Upon entering the inoperable state, a branch element is evaluated to identify the reason for

the disturbance. In case of a technical failure, the state malfunction is entered. If grout is not

available but the production process could still start, noGrout is entered. This distinction

provides the possibility to evaluate the explicit reason for downtime.

When fault remedy is finished or fresh grout is delivered, the system element is functioning

again and the inoperable state can be exited. The state operable is entered via a history state that

resumes the last state active in the compound state.

Figure 40: Enhanced SysML stm of the backfill grouting system to consider disturbances

In order to model and quantify the disruption of the two production processes, an abstraction

must be done to identify disturbances within a specific element. The execution of the production

process advance is associated with the cutting wheel, since this element excavates muck and

thus enables the advancement. To perform advance, the elements cutting wheel, thrust cylinder,

backfill grouting system, and mucking device of the TBM must be operable. As soon as one of

these elements enters the state inoperable, the production process advance must be suspended

or will not start in the first place. To analyze this scenario and quantify the different

disturbances, the element cutting wheel is used as a representative. If either one of the other

three elements is inoperable, cutting wheel is also inoperable.

The second production process, ringbuild, is associated with the element erector, because

segment installation is performed here. In order to perform ringbuild, the erector, the thrust

cylinders, and a segment-handling device on the TBM must be operable. The segment-handling

device moves segments to a location where the erector can reach them. The erector puts them in

place and the thrust cylinders temporarily secure them.

The enhancement state machine is shown by example of the erector in Figure 41. The

exchange of signals is again not shown. The state inoperable was not modeled in the original

SysML state machine diagram. Now, inoperable distinguishes between a malfunction (i.e.

idle

grouting

stm Process description [Processes executed by backfill grouting system distinguishing disturbances]

operable

malfunction[technical failure]

H*

inoperable

[! functioning]

noGrout[! grout available]

[functioning]

[87]

technical failure) of the erector itself and an unfulfilled precondition, i.e. precondFailure. The

required preconditions are the functionality of the thrust cylinders and the segment-handling

device, as well as the availability of segments to start at all. If either of these preconditions is not

met, the variable functioning is false and the state machine enters the inoperable state. Whether

the sub-state malfunction or precondFailure is entered depends on the specific reason that

functioning was set to false. If a technical disturbance occurred in the erector itself, the

malfunction state is exited after fault remedy. When it is exited, a branch evaluates whether all

the preconditions are met. This ensures the consideration of additional downtime due to other

reasons, e.g. segments are not delivered timely. The compound state inoperable can be exited

only if all other preconditions are fulfilled. If unfulfilled preconditions evoked the disturbance in

the first place, the inoperable state can be left directly after troubleshooting.

Figure 41: Enhanced SysML stm of erector to consider disturbances during ringbuild

5.2 Disturbances related to the supply chain

All processes not directly related to production are generally referred to as support

processes. Typical support processes include delivery of material, the deployment of capacity, or

the extension of the supply chain. Common examples problems in the supply chain that cause

standstills are delays of segment and/or grout delivery, problems during the installation of

additional rail tracks, or the extension of slurry pipes. Support processes and production

processes are often performed in parallel. In consequence, production is not directly affected by

the failures of support processes. For example, if a disturbance related to the element slurry

idle

stm Process description [Processes executed by erector including disturbances during ringbuild]

operable

malfunction[technical failure]

H*

inoperable[! functioning]

precondFailure[! precondition]

pickUp

ringbuild

install

[ring complete][else]

[functioning]

[88]

circuit occurs during ringbuild, the production process is not affected. However, production

stops if the required material or capacities are not available timely or adequately. Thus, the

production process advance cannot begin directly after ringbuild is finished unless the element

slurry circuit is fully operational.

This short description reveals that support processes often include a certain period of time to

allow for troubleshooting. The corresponding production process is affected only if this time is

exceeded. A disruption in segment-handling has no immediate impact on ringbuild unless the

last delivered ring is not installed completely. However, if the disturbance exceeds a full advance

cycle, production must be stopped because there are no more segments to install. This duration

can be considered as the buffer time for troubleshooting. For EPB shield machines, the

availability of muck cars is often a critical bottleneck. Frequently, performance losses in projects

can be observed due to inefficiencies in mucking capacities.

Obviously, production requires specific resources, storage volumes, and material to be

executed. If these preconditions are not met, the process is not executed, resulting in downtime.

In order to model disturbances resulting from supply chain problems, two specifications must

be considered: the preconditions to start a process and the interdependencies between elements

that express these preconditions. The dependency between production and support processes

and resulting disturbances can be modeled with these two aspects. The process

interdependencies are expressed in SysML sequence diagrams. The preconditions are already

modeled in the SysML state machines shown in Chapter 4.

5.3 Cascading disturbances

Most disturbances simply delay the project progression. There are some disturbances,

however, that might cause additional disruptions to the project. If the intensity of a disturbance

exceeds a specific threshold, it affects the functionality of other elements. Additional tasks are

then required to resolve these additional disturbances. The threshold to provoke the second

disturbance can be directly expressed by the duration of the first disturbance. Thus, if the

duration of the first disturbance is lower than the specified threshold, the additional disturbance

is not provoked.

The circumstance in which one disturbance provokes the emergence of another disturbance

is referred to as a cascading disturbance. The occurrence of a cascading disturbance is

qualitatively depicted in Figure 42, which shows the production and disturbances on an

unscaled timeline. During Advance 2 (Adv2), Disturbance A arises. After troubleshooting,

Advance 2 is continued. Disturbance A occurs again during Advance 3. But this time

troubleshooting takes much longer, i.e. the duration of the disturbance is much longer and a

certain threshold is exceeded. This provokes the emergence of Disturbance B. Consequently,

production can only be resumed after both disturbances are remedied.

[89]

Figure 42: Qualitative visualization of a cascading disturbance during advance

This thesis presents a concept to analyze such cascading disturbances. The concept was

successfully presented to the academic community in a peer-reviewed publication [Rahm et al.,

2016]. It is exemplified by an issue triggering a cascading disturbance. Additional dependencies

between disturbances that might provoke a cascade can be modeled accordingly.

The exemplified issue is based on the processing time of the grouting mortar used for

backfilling. The mortar slowly hardens over time. It becomes stiffer and its flowability gets

reduced. Thus, after mixing, it must be injected into the subsoil within a certain period of time.

Otherwise, it might clog the pipes of the backfill grouting system and eventually dry out. If this

happens, the pipes have to be dismantled and changed, resulting in a major damage with

tremendous loss of time. This extraordinary event of pipe replacement is not modeled in detail.

Only the counter measurement to avoid it, the exchange with fresh grout, is simulated. So, if a

disruption occurs with ready-to-use grout already delivered to the machine, the grout must be

disposed of timely, i.e. within its processing time, to prevent damages to the grouting system.

The reason leading to an exceeded processing time can be a technical or electrical failure that

causes a delay of grout delivery or suspends the advance process. Furthermore, the beginning of

advance can be delayed even when grout is already available. This might happen, for example, if

the extension of slurry pipes or ringbuild is unusually prolonged and delays the beginning of the

next production cycle. If this happens and the processing time is exceeded, the hardening grout

must be disposed of, the container cleaned, new grout mixed and loaded onto the vehicle and

finally delivered to the TBM. Only after these additional processes are completed can advance

continue or start at all.

In order to model cascading disturbances, a detailed analysis of the relevant process

interaction is necessary. SysML sequence diagrams are used for this reason. Figure 43 depicts a

cascading disturbance in a SysML sequence diagram. In the presented example, the processing

time of grout is exceeded due to a disturbance in the train during material delivery.

The vehicle is loaded by the crane. After the crane signals that the loading process is finished,

the vehicle leaves for the TBM. If a disturbance occurs while driving, the SysML optional

interaction operator [opt] is entered. This evokes the beginning of a repair process. When fault

remedy is finished, two alternatives can take place. If the duration of the disturbance has not

Production

Pro

cess

es

Downtime DowntimeProductionTime

RB1

Adv1 Adv2

RB2

Dist A

Dist B

Adv2

Dist A

Disturbance Threshold

Adv2 Adv2

[90]

exceeded the processing time, the vehicle will continue to the TBM. If the duration has exceeded

the threshold, however, the vehicle returns to the jobsite. Then, the old grout is disposed and

new grout is mixed and loaded onto the train. Finally, the sequence is restarted at the signal that

the loading process is finished. The vehicle leaves again for the TBM. When it arrives there, the

vehicle notifies the system accordingly, i.e. DeliverG. This evokes further actions which are not

displayed here.

Figure 43: SysML sd showing cascading disturbances affecting ringbuild

sd Process interaction [Interaction during ringbuild in case disturbance occurs]

: Vehicle : Gantry Crane : Concrete Plant

[disturbance occurs]opt

: Grout Pump

LoadFin

alt [processing time not exceeded]

Continue

[else]Return

Return

DisposeGroutMixGrout

NewGroutLoadFin

DeliveryG

[91]

6 Operational input data for the developed simulation approach

Chapter 3 described two procedure models to conduct simulation studies. Both highlight the

importance of input data. The collection of project data and the structuring and processing to

derive applicable input data is a difficult yet crucial step. This chapter describes the acquisition

of project data and the processing of input data for the subsequent case study.

6.1 Acquisition of project data

Modern TBMs are equipped with several hundreds of sensors to monitor operational

parameters of all kinds. These parameters include temperatures, pressures, volume flows, and

cylinder extensions, to name a few. The specific number of sensors depends on the type of

machine and the manufacturer, but generally shows an increasing trend. Additionally, a

navigation system constantly monitors the position and orientation of the TBM to ensure correct

driving.

The sensor values are read in sub-second intervals by the programmable logic controller

(PLC) of the TBM. The high number of sensors, combined with the low resolution of sensor

measurements, results in a very large amount of data. Apart from the sensor values, the PLC also

registers the current state of the TBM. Usually, three states are distinguished: advance, ringbuild,

and standstill. The latter is defined as neither advance nor ringbuild. The states are the result of a

certain combination of sensor values and control switches. They can be used to evaluate sensor

measurements in combination with the TBM’s state, e.g. the pressure in excavation chamber

during ringbuild versus the pressure during advance.

Unfortunately, automatically monitored values are mostly related to mechanical aspects of

the machine, such as pressure and temperature. Particularly, logistic processes are not recorded

automatically. Such information is noted manually by a work shift engineer or foreman and is

thus subject to manual time measurements and judgment.

The best practice for project work shift documentation in mechanized tunneling is the use of

a coded classification of events and processes. These codes describe all the types of work

performed during project execution. All time-consuming events are assigned to an event code

and noted in a work shift report. Apart from the production processes, this includes

maintenance, clearance of blocked slurry lines, repair of train derailment, extension of supply

pipelines, and so on. Table 2 provides a short example through a tabular representation.

Table 2: Exemplary export of work shift report in tabular format

Report Nr StartTime EndTime Duration Code CodeText CommentReport-1234 03:00:00 04:00:00 60 302 Extend air and water lines Pipes extension

Report-1234 03:00:00 04:00:00 60 200 Ring build Ring-75

Report-1234 04:00:00 06:20:00 140 100 Advance Ring-76 / Mining complete

[92]

The first column allows the association to the specific shift. Then start and end time of the

event are logged. In the example, information concerning dates is removed. When processing the

data, a change in the date must be handled accordingly in order to calculate the duration of the

process correctly. The code and corresponding text allow the classification. Additional

information can be logged in a comment field.

Traditionally, these reports have been written on paper. Recently, Excel-based templates and

specialized software are used to generate the reports digitally. This facilitates the evaluation of

the reports to a great extent. Mayer et al. [2014] present a sophisticated software application for

work-shift documentation. For usability reasons, the shift reports can be filled out in Gantt-

charts. The web-based software application enables shift reporting by dragging and dropping on

a canvas or by explicitly stating the start and end of an event. The TBM’s states, recorded by the

PLC, are imported to the software application. This facilitates the classification of events and

especially standstills.

Even such sophisticated software applications, however, are not designed for automatic data

processing or analysis but rather for informing decision-makers about current issues and the

general project status. Additionally, the code list frequently exceeds 80 classifications and is thus

prone to individual judgment. What is more, the event codes are different for different machines,

countries or companies. This lack of transparency makes performance comparisons quite

difficult. Consequently, a lot of manual work and sensible judgment are required to prepare data

for further application.

6.2 Introduction to reference project

In order to provide a tangible example of the developed simulation approach, a reference

project is introduced. A typical metro project is chosen where a slurry shield machine was

applied. Unfortunately, permission to state the origin of the project data has not been given and

the project must thus remain anonymous.

A schematic and unscaled representation of the geotechnical cross-section is provided in

Figure 44. The tunnel features an outer diameter of 6.6 m and is approximately 1 km long. The

specific length is abstracted to conceal the actual project. The tunnel is built in homogenous soil.

Thus, impact of changing soil conditions does not apply.

The segmental ring is based on a 6+1 design. The width of one segmental ring is 1.5 m. The

segment-handling device of the backup system features a segment feeder to store a full ring.

Thus, efficient and fast ringbuilding is done without major interference from the delivering

vehicle. The grouting system is based on a 1-C-grout to fill the annular gap. The surface jobsite

has one gantry crane and storage areas for all necessary materials. The 1-C-grout is mixed in the

grout batching plant at the jobsite. The grout is transported to the TBM by trains. There are two

trains available for material transports. The grout is then pumped into a tank on the backup

system, where it remains until consumption.

[93]

Figure 44: Unscaled representation of reference project as cross-section

The total production makespan was 205 days. Construction took place 24 hours a day on all

seven days of the week. Out of this total project duration, 58.6 days were taken up by advance.

This is due to the comparatively slow advance rate of 10 mm/minutes on average. The

installation of the segmental ring has a total duration of 23.5 days. Consequently, about 122.2

days of the total makespan were unproductive. The actual reasons for this high ratio of

downtime remain confidential.

The presented data represents the possible production period. Thus, assembly and

disassembly of the machine is not included. Furthermore, major modifications or administrative

issues resulting in prolonged downtime are also not considered. Therefore, the share of

unproductive time is related solely to operational issues that can be addressed in the presented

simulation approach. Figure 45 shows the allocation of advance, ringbuild, and downtime of the

reference project.

Figure 45: Time allocation of production processes and downtime of the reference project

6.3 Deriving input data

Distribution fitting methods were applied to derive adequate input data. This task is crucial

to perform sound simulation studies. The availability of representative data to parameterize

models is often a limiting factor for simulation studies. In order to justify the application of

1000 meter

6.6 meters

Downtime122.2 days

Ringbuild23.5 days

Advance 58.6 days

[94]

sophisticated probability distributions (e.g. Beta, Johnson SB) a large data set is required. The

professional statistical software application Expert Fit, used for distribution fitting, is introduced

hereafter.

The main goal of the presented approach is the analysis of production disturbances.

Therefore, specific information about the production itself is required. Thus, a first step is the

derivation of information about the two production processes from the reference project’s shift

reports. Following this, the occurrence and extent of disturbances are derived in separate

probability density distributions.

6.3.1 Distribution fitting with ExpertFit

Identifying a well-suited theoretical distribution is supported by several software systems.

The presented approach makes use of the well-established commercial software application

ExpertFit [Averill M. Law & Associates, 2016]. The software has been continuously enhanced

over the past 20 years. Algorithms automatically fit the probability density distributions of the

underlying data set. The evaluation of 40 distributions is possible and graphical comparison

with the sample set is supported. A ranking of fitted distributions facilitates the decision.

Additionally, statistical goodness-of-fit tests are incorporated. The application supports the

application of the fitted distributions as input statement for several simulation packages,

including AnyLogic. In the case of AnyLogic, the input statement is a method of class

java.util.Random with the corresponding parameters. A detailed description of the software and

the implemented workflow and algorithm is given in the work of Law [2014]. In this thesis, the

workflow is only briefly described.

The project data set can be entered into ExpertFit application as comma-separated values or

via clipboard. The software analyzes the sample set and provides suggestions for histogram

classification. This classification can be modified to generate a smoother shape of the histogram.

The application can automatically fit several distributions and provide a ranking. Alternatively,

the user can specify the distributions to be fitted. The software also recommends the use of

empirical distributions if the highest-ranked distribution is not suitable for application in

simulation studies. Following this, distributions can be graphically compared with the

underlying data set. A very good first impression of how well a distribution fits a sample set is

provided by a density-histogram plot. Here, the histogram and the fitted probability density

function are plotted together and deviations are visible at first glance. A more sophisticated

method of evaluation is provided by a statistical goodness-of-fit test. ExpertFit conducts three

such tests: chi-square test, Kolmogorov-Smirnov-Test, and Anderson-Darling test. These tests

indicate whether a certain distribution is suitable for representing the underlying data or it

should be rejected. Within the presented thesis, only limited project data can be applied.

Consequently, the goodness-of-fit tests are not used for rejecting data as there is no alternative.

However, the focus of the presented approach is the presentation of simulation-based approach

[95]

to quantify operational disturbances and not the accuracy of probability distributions to

randomly generate them during runtime.

Figure 46 shows a density-histogram plot to compare fitted distributions in terms of the time

to recover (TTR) in the system element erector. The underlying data set is provided by the grey

histogram. Additionally, four fitted probability density functions are plotted in varying colors.

The plot shows a rather good representation of all of the fitted distributions. Exceptions are

visible at the end of the distribution. This indicates very long disturbance times. Such

extraordinary shapes cannot be expressed by standard probability distribution functions. If

desired, empirical distributions can be applied to represent this data set. However, probability

distributions usually have a long tail to the left. Consequently, the generation of large values is

possible and should in fact rather be reduced to a reasonable length, to avoid freak values.

Figure 46: Graphical comparison of distribution fitting with density histogram plot

Other graphical comparisons are also provided by the software, such as frequency

comparison, distribution-function-difference plot, and probability plots (quantile-quantile and

probability-probability). These methods allow the first evaluation of the fitted distribution,

especially if there are several choices. Figure 47 shows the frequency-comparison plot of the

distributions to represent the TTR of erector. The fitted distributions are displayed as histogram

to match the representation of the underlying input data.

The representations of the distribution-function-difference plot and the probability-

probability plot for the given example are attached in Appendix E. Additionally, Appendix E

includes the density distribution plots of all derived probability distributions functions.

12 intervals of w idth 25 1 - Inverse Gaussian 2 - Pearson Type VI(E)

3 - Johnson SB 4 - Lognormal

0,00

0,05

0,10

0,15

0,20

0,25

0,30

0,35

0,40

17.50 67.50 117.50 167.50 217.50 267.50

Density

/Pro

port

ion

Density-Histogram Plot - Erector - TBF - ComparisonDensity-Histogram Plot - Erector - TTR- Comparison

Interval Midpoint

[96]

Figure 47: Graphical comparison of distribution fitting with frequency-comparison plot

6.3.2 Production-related input data

For advance and ringbuild the durations can be derived directly from the exported data as

they are recorded by the PLC automatically. However, if mining is intermittent, the different

entries must be manually merged into a single entry. After the condensation of divided entries,

the sample data is entered in ExpertFit and represented in histogram classes. Outliers in the

sample set are identified easily in the histogram. Such freak values can be extraordinary events

or errors during data acquisition or preparation. Depending on the reason, the values may or

may not be excluded from the sample set.

The parameters of the derived probability distribution functions are shown in Table 3. The

process advance is represented by the recorded advance rate. Ringbuild is modeled separately

for each segment. Therefore, the probability distribution represents the duration of a single

segment installation. Additionally, Table 3 shows the probability distribution function needed to

model the extension of the slurry pipes.

Table 3: Probability distribution functions to model production and logistic processes

Process Distribution Parameter

Advance rate Weibull loc = 0.00; α = 2.58; β = 17.41

Segment installation Log-Logistic loc = 0.00; α = 4.31; β = 5.54

Pipe extension Log-Logistic loc = 9.47; α = 2.02; β = 70.69

6.3.3 Disturbance-related input data

The developed approach focuses on the simulation of operational disturbances within a

complex system. The consideration of technical failures requires two parameters. First, the

12 intervals of w idth 25 1 - Inverse Gaussian 2 - Pearson Type VI(E)

3 - Johnson SB 4 - Lognormal

0,00

0,05

0,10

0,15

0,20

0,25

0,30

0,35

17.50 67.50 117.50 167.50 217.50 267.50

Pro

port

ion

Frequency-Comparison Plot - Erector - TBF - ComparisonFrequency-Comparison Plot - Erector - TTR- Comparison

Interval Midpoint

[97]

occurrence of disturbances must be modeled. The reference magnitude can be based on time,

length, strokes, rotations, executions, or other events. In the stationary industry, failure rates are

often related to items produced. The approach at hand uses a time-based reference magnitude to

express the occurrence of failures. The TBF is derived by the distance between the end and start

of two subsequent events with the same code in the shift report. The second parameter required

to simulate the effect of disturbances is the duration of such an event. This is referred to as TTR

and is the difference between start and end time of the same event instance. In industry, the

terms mean TBF and mean TTR are generally used for these two parameters. The danger of

substituting a probability distribution with the corresponding mean is vividly exemplified in the

work of Law [2014]. Bottlenecks might not be identified in the simulation study, as the mean

value could be just under the critical threshold. Consequently, the input data is expressed by

probability distribution functions and the word mean is omitted.

For each simulation component, two distinct probability distribution functions are derived to

express TBF and TTR respectively. Failures of subassemblies are not explicitly modeled in the

presented approach; they are assigned to the main assembly model element as a step of data

structuring. Thus, all reasons for disturbance can be taken into account. This requires sensible

mapping and merging of coded events in the work-shift reports.

[98]

7 Implementation in a simulation framework

The following chapter describes the implementation of the presented approach in a

simulation framework. In Chapter 2, the author discusses academic simulation frameworks used

for simulation-based approaches in the construction industry and highlights the associated

drawbacks. In order to circumvent these drawbacks, the presented approach is implemented in

the commercial simulation framework AnyLogic [XJ Technologies, 2015]. The concepts of

modeling processes in AnyLogic and SysML are almost identical. This highly facilitates the

verification of formal model description in terms of implementation in the simulation model.

Additionally, the transparency of the developed model is very high and allows independent

assessment by users with limited simulation experience.

The chapter starts with an introduction to the AnyLogic simulation framework. Following

this, the implementation is presented in detail. The approach is based on distinct simulation

components. This modularization ensures flexibility and reusability and facilitates further

developments through expandability. After introduction to modularization, the behavioral

implementation is described. This section also describes the flow of material in the simulation

model. Special focus is put on the simulation of continuous material, which is enabled by a multi-

method simulation approach. The flexible implementation of process interactions in a modular

simulation concept is then described. The concept of random sampling with AnyLogic is

addressed separately. The chapter closes with the description of the implementation to simulate

operational disturbances.

7.1 AnyLogic simulation framework

AnyLogic [XJ Technologies, 2015] is a general purpose simulation framework that also

supports the creation of encapsulated special purpose simulation applications. It is possible to

bundle such applications to allow stand-alone execution of simulation experiments. AnyLogic

supports the three most commonly applied simulation methodologies: discrete-event simulation

(DES), system dynamic, and agent-based modeling. The seamless combination of methodologies

in a single simulation model is provided. This enables the modeler to apply the most suitable

methodology to express system characteristics. Consequently, the need to abstract

particularities in order to fit into a certain simulation framework is highly reduced. The level of

detail can be adjusted to the defined objectives; it is not determined by the underlying

simulation methodology.

AnyLogic is based on a native Java environment and allows the use of all available libraries

and resources. The adaptation of basic model functionalities by custom Java codes is possible.

The definition of Java classes, including inheritance of AnyLogic standard classes, is also

provided. Thus, AnyLogic consequently supports object-oriented modeling designs that facilitate

the creation of modular and incremental structures in large models. This flexibility enables the

[99]

modeler to create very sophisticated models and ensure flexibility, reusability, and

expandability through modularization.

Additionally, a wide range of predefined tools and library objects allow quick model

development for several applications, including logistics, manufacturing, and pedestrian

simulation. The graphical user interface also supports the use of general model elements such as

variables, events, and methods of object-oriented programming.

Furthermore, AnyLogic readily supports animation of 2D and 3D objects by accessing states

and property values of simulation objects during runtime. For all calculated variables, e.g. time

remaining for a certain event to happen and state-dependent parameters like different

maximum velocities for a loaded or empty vehicle, are displayed during runtime with the

current numeric value. The access and visualization of all intermediate simulation states, as well

as specific values, is a key factor of model validation and allows sound investigations. Obviously,

the 2D and 3D animation methods are also very useful for demonstration and representation

purposes of the developed simulation model.

Moreover, uncertainty of input data can be addressed by random sampling in the Monte-

Carlo simulation experiments. Alternatively, the measured values in the underlying sample set

can be used directly for experiments. It should be mentioned that the application of optimization

algorithms in simulation studies is also supported, even though optimization is not applied in

the presented approach.

Finally, the most important reason for choosing AnyLogic is the possibility to use state-charts

to express procedural behavior. Fundamentally, state-charts are equivalent to the state machine

representation of SysML, with the additional possibility to execute them in a DES environment.

The concept and graphical representation of states and transitions are quite the same.

Furthermore, both methodologies support the possibility of encapsulating element specific

behavior. This further facilitates the development of modular, and thus easily reusable, special

purpose simulation models. A difference is found in the exchange of signals between elements.

In SysML, the exchange of messages is graphically notated, but it is coded in AnyLogic state-

charts. Nevertheless, the transitions, as the place where these messages are sent or received, are

the same. Consequently, the similarity between AnyLogic’s state-charts and SysML state

machine diagrams highly facilitates the verification of conceptual models in terms of

implementation.

7.2 Random sampling in AnyLogic

Random variables generators integrated in simulation frameworks are used to sample values

from probability distributions. AnyLogic readily implements class java.util.Random [XJ

Technologies, 2015]. This standard package applies a 48-bit seed with a modified linear

congruential formula. Details of the linear congruential formula and similar approaches can be

found in the work of Law [2014]. The basic idea is that a seed value is used to generate the first

random variate from the probability distribution. The following sequence of random variates is

[100]

based on this seed value. If the method of generating sequences remains unchanged, a specific

seed will generate a specific sequence of “random” numbers. Thus, the sequence of random

numbers generated during execution is reproducible. For this reason, the term pseudo-

randomness is often found in literature. This is especially important if a simulation is executed

several times to search for mistakes in the implementation. In Monte-Carlo experiments, the

seed value is alternated.

At this point it must be stated, that ExpertFit, the professional statistical software application

used for distribution fitting (see Chapter 6), includes very sophisticated distributions. Some of

the more elaborate and unusual distributions are not supported by the standard class

java.util.Random. The author has checked academic packages for generation of random variables

to see if the required distributions are supported. Test implementations with AnyLogic were

successful. However, the author has decided to apply a probability distribution supported by the

class java.util.Random and neglect small errors in the input data, as the simulation-based

approach is the focus of this thesis and not high-resolution representation of input data.

7.3 Structure of simulation modules

Chapter 4 presents the formalization of the mechanized tunneling domain in the graphical

modeling notation SysML. This transparent and comprehensive description now facilitates the

transfer of the formal system in a computer-based simulation model. The developed simulation

model closely relates this to SysML formalization, which in turn facilitates the verification

process. The general hierarchical composition of the domain (see Figure 10) is maintained.

Consequently, the first structural tier is based on the four elements: TBM, backup system, tunnel,

and surface facility. The elements are implemented as distinct classes that are referred to as

“agents” in AnyLogic. The term “agent” is somewhat confusing since it implies the use of the

agent-based simulation concept. However, agents are the basic building units of AnyLogic and

the corresponding model design. In AnyLogic, “agents” are characterized by distinctive behavior,

memory (in sense of history), contacts, and timing [XJ Technologies, 2015].

The two external domains provided by the subterranean and the surface environment are not

investigated in detail. Only the influence resulting from geotechnical conditions is considered

within the presented approach (see Rahm et al. [2012] and [2013]). Dependencies entailed by

the surface jobsite, surface buildings, or the external supply chain are not included in this study,

which focuses on other research activities (see Scheffer et al. [2016] and [2014]).

The presented approach is based on distinct simulation components. The dependencies and

interactions between these simulation components are described in Section 7.4. Hard-coded

structures are thus avoided. This allows the flexible exchange of components in order to define

alternative machine setups and supply chain configurations. Similar to the SysML formalization,

the second level of hierarchy allows setup alternatives by exchanging elements.

Figure 48 shows a screenshot of the developed simulation model, including the main

elements, project properties, and reports during runtime. The hierarchical composition

[101]

identified in the system analysis is transferred and visible at first glance. Additionally, Figure 45

also provides insight into the main element TBM, and thus to the second level of hierarchy. This

main element is composed of five sub-elements: cutting wheel, thrust jacks, erector, slurry circuit,

and system. Similar to the main elements of the model design, all simulation components are

implemented as AnyLogic Agents. They thus resemble a distinct class of elements that can be

parametrized or replicated.

The presence of the simulation component slurry circuit indicates that the shown simulation

model represents a Slurry shield machine. All relevant simulation components identified in the

system analysis for TBM, backup system, tunnel, and surface facility are implemented as

simulation modules. Additionally, components for modeling the influence of geotechnical

aspects and visualizing gathered simulation results are realized.

Figure 48: Screenshot of developed simulation components and the hierarchical structure

Figure 49 depicts the second level of hierarchy of an EPB shield machine. The previously used

simulation component slurry circuit is exchanged by the simulation component screw conveyor.

This assembly component is typical for EPB shield machines.

Figure 49: Screenshot of interior setup of TBM to model an EPB shield

[102]

The relation to the formalized system description is again clearly visible. The link from

cuttingWheel to screw conveyor indicates a material flow, in that case muck. The circles in this

material flow simulation correspond to the in and out ports of the SysML internal block

definition notation. The connection, on the other hand, corresponds to the flow without

specifically labeling the handled material.

In Figure 50, a screenshot showing the internal view of the backup system is presented. The

figure depicts all components of this main element instead of a specific setup. In order to model

a specific setup, only one of the three mucking devices can be used. The element grout pump is

mandatory for the simulation. If grouting is based on 1-C-grout, the grout pump transfers the

material from the delivering vehicle to the internal storage. If 2-C-grouting is applied, the grout

pump is modeled to transfer the hardening agent. The continuous delivery of the base grout is

not modeled explicitly.

The setup of the segment-handling device for the backup system allows certain alternatives.

The segment crane is an obligatory element that unloads the segmental lining from the

delivering vehicle. The segment feeder on the other hand is without obligation. The mucking

device of the backup system provides again three possibilities. The first two, i.e. backup belt

conveyor and tunnel belt conveyor, can be applied in EPB shield machines. The slurry circuit is

only applicable in slurry shield machines

Figure 50: Screenshot showing all simulation components applicable for backup system

[103]

The main element tunnel is implemented for defining major parameters and visualizing the

progress of the project. The internal view of this main element is thus not detailed, as it would

depict only the parameters corresponding to the respective SysML block definition diagram.

Figure 51 provides the internal setup of the surface jobsite facilities. Two basic simulation

components have been developed, namely vehicle and gantry crane. Both can be replicated to

allow for parallel transportation and un-/loading sequences. The external supply chain is not

considered in the presented simulation approach. Thus, limitations resulting from insufficient

stock or capacities of the surface jobsite are not modeled here. The crane can access an

unlimited stock of segments and mortar for loading onto waiting vehicles. The vehicle is

characterized by the parameters identified in the system analysis. Depending on the type of

mucking, it does or does not provide mucking capacity.

Figure 51: Screenshot showing all simulation components for surface jobsite facilities

7.4 Behavior of simulation modules

The processes of simulation components are implemented in a plain DES concept provided by

AnyLogic. The implementation closely follows the state machine diagrams of the SysML notation.

However, state machines are referred to as state-charts in AnyLogic. The basic concept is the

same. In this section, the process tier of simulation components is detailed exemplarily to avoid

repetition. The remaining state-charts are modeled according to their SysML representation.

7.4.1 Implementation of processes

A screenshot of the state-chart for the simulation component erector is shown in Figure 52.

The implementation highlights the close resemblance to the SysML formalization. The entry

point state-chart initializes the process simulation. The remaining elements are identical to the

SysML formalization. The time-dependent change of the currently active state is highlighted by a

thick red border. Following this, the active state is assembly, as shown in Figure 52. The superior

compound state encompassing the active state is also color-coded. This simple graphical

[104]

visualization helps inexperienced planners to understand and analyze the system during

runtime. Additional graphical support is provided by the visualization of current values. The

transition is triggered in 2.348 minutes of exiting the state assembly. The duration of segment

installation is randomized based on the underlying input data. The model time unit is minutes

since it is the common unit for durations in mechanized tunneling. Furthermore, it provides a

convenient mix of accuracy and manual measurability. Further information derived from the

screenshot is that for the current ringbuild, four more segments must be assembled. Following

that, the state after assembly is pickUp. Additionally, it can be seen that the tunnel is currently

247.5 m long. The presented state-chart features one difference compared to the SysML stm.

When the compound state ringbuild is exited, an additional branch is entered. If the tunnel length

is reached, the branch leads to a final state element (solid, black point with an encompassing

circle). This is required to finalize the simulation experiment. When this state is reached, all

information is gathered conclusively and the engine is terminated afterwards. Obviously, this

feature is only required for implementation purpose, just like the methods isOperable and

update. Java methods are called functions in AnyLogic. Hence, the letter “F” is used in the icon.

There are many more of these implementation-specific elements, especially control variables

and methods that are not displayed explicitly.

Figure 52: Screenshot showing the internal setup of the simulation component erector

7.4.2 Implementation of material flow

The cargo items, i.e. segments and container, are modeled as distinct Java classes with

attributes corresponding to the SysML block definition diagrams provided in Chapter 4. During

runtime, the objects of these classes are instantiated and transported within the model. Since

cargo items represent a distinct and tangible entity, the DES concept can be applied easily to

simulate the handling of these elements.

However, the flow of liquids raises a problem in DES. Liquids cannot be viewed as distinct

entities unless they are contained in a tank or something similar. The transport of muck with a

tunnel belt conveyor thus provides difficulties for implementation. The same applies for the

circulation of slurry and the transfer of grout. The plain discrete-event concept of state-charts is

not quite adequate for modeling a continuous flow of materials like these. For this reason, a

[105]

multi-method approach is pursued. Materials that can be viewed as distinct entities are

implemented in the DES concept while all continuous material flows, e.g. pumping of slurry or

excavation of soil, are implemented through a system dynamic concept. The multi-method

simulation concept is published by Rahm et al. [2012].

Figure 53 visualizes the implementation of the multi-method approach for the simulation

component cutting wheel. The two distinct model tiers are indicated.

Figure 53: Screenshot showing the implementation of the multi-method simulation concept

During runtime, the DES model tier is initiated. When the state advance is entered, the system

dynamic model tier is triggered. Based on the dimensions of the segmental ring, a certain volume

must be excavated in one advance step. The corresponding value is calculated and assigned to

the stock variable (VolumeAdvStock). This can be randomized to account for variations due to

soil layer changes. Additionally, a flow rate is calculated on the basis of the current advance rate.

The flow rate is calculated by multiplication of advance rate and excavated area. The flow rate is

calculated according to the following formula:

[ ⁄ ]

⁄

The screenshot in Figure 53 shows that the simulated cutting wheel has a diameter of 9.49 m

and the current advance rate is 36.874 mm/min. This implies a flow rate of 2.608 m³/minutes.

According to the system dynamic concept, the flow rate extracts units from an associated stock

and transfers them to another stock. In Figure 53, 2.608 m³/minutes are removed from the stock

VolumeAdvStep every minute and transferred to the stock TotalVolumeExcavated. When the

remaining volume of 87.579 m3 is excavated completely, the system dynamic model tier triggers

the transition from advance to idle. If a disturbance occurs during advance, the flow rate is set to

DES model tier

System Dynamic

model tier

[106]

zero and no more material is removed. After fault remedy, the flow rate is set back to the

previous values. Typically, the advance rate is not constant in a single advance step. These

variations could be simulated easily with the multi-method approach. However, this specific

issue is not considered in the presented approach due to lack of significant data to justify such

variations.

The combination of these two simulation concepts is supported by AnyLogic and provides

three major advantages. First, the simulation of liquid material as a continuous flow is the

correct and natural reproduction. While its abstraction in a plain DES environment is possible,

the continuous simulation is better and more vivid for the user. Second, the simulation of

uncertain variations of the advance rate within each excavation step is possible. The third

advantage is related to the consideration of disturbances and is depicted in Figure 54. It

compares the implementation of the soil excavation in a plain DES with the multi-method

approach. If a disturbance suspends the process excavating in the DES approach, the process

excavating must be split up. The remaining amount of work must be stored in a variable and

used as starting point for the process excavating 2. If another disruption occurs, the procedure

must be repeated again. In the multi-method approach, the system dynamic model can be

viewed as a progress bar. The excavation of soil is simulated continuously. Thus, when a

disturbance occurs, the system dynamic model is paused. The flow rate is set to zero and the

volume to be excavated remains unchanged. When fault remedy is done and the excavation can

continue, the same process can be reentered and the system dynamic model resumed. This can

be done multiple times.

Figure 54: Comparison of plain DES and the developed multi-method approach

A system dynamic model tier is integrated in all components related to the continuous

transport of material. For the presented approach, these materials are muck, slurry, and grout.

The simulation of slurry flow by a system dynamic model tier could be extended in the future to

include the dimensioning and utilization of the soil treatment plant. The holistic consideration of

such influences will further increase the accuracy and area of application of the presented

approach.

[107]

7.5 Process interactions between simulation modules

In the system analysis the process interactions between the various elements are modeled by

SysML sequence diagrams. The dynamic behavior of systems is based on the exchange of

information. Therefore, it is a central aspect of the presented approach. In order to enable the

flexible exchange of simulation components, a central distribution manager is supposed to

distribute information accordingly. For this reason, the Observer-Observable design pattern is

applied and a central event manager is developed as distinct Java class. This event manager

controls the communication between the various elements. The simulation components register

specific signals with the event manager. All information is sent to that event manager, which then

distributes the signals accordingly. Consequently, there are no hard structures between the

particular simulation components. This flexible and centralized communication structure is the

key to the exchange of simulation components in the modularized concept.

The concept of the event manager is visualized in Figure 55. At first, all components register

themselves for specific signals. This is depicted only for the element erector, which registers the

signals (AdvFin and SegAvail). The list of observers is updated in the event manager. In the

example given in Figure 55, the elements erector, grouting system, and thrust cylinders are

registered for the signal AdvFin. Furthermore, the signal SegAvail is observed, i.e. expected, by

the elements erector and segment crane.

Figure 55: Conceptual visualization of the event manager for flexible process interaction

During runtime, information is passed between the elements. In Figure 55 the cutting wheel

signals the end of the production process advance (AdvFin). This signal is sent to the event

manager instead of being sent directly to the specific elements. The event manager then iterates

through the list of observers. If an observer is registered for the particular signal, the

corresponding element is notified. In the shown example, the three observers for the signal

AdvFin are updated with the corresponding signal. If an existing simulation component is to be

Erector Thrust cylindersGrouting system

update(AdvFin)

Cutting wheel

send(AdvFin)

Signal Observer

AdvFin

SegAvail

erector, backfill grouting system, thrust cylinder

erector, segment crane

Event manager

register(AdvFin)

register(SegAvail)

[108]

redesigned or a new one is to be developed, the consistency of this communication structure

facilitates the integration. Therefore, the central event manager is the foundation of the modular

simulation approach that reduces the modeling effort significantly.

7.6 Implementation of disturbances

The main goal of the presented approach is the quantification of performance losses due to

operational disturbances. The implementation of disturbance modeling is depicted by the

screenshot in Figure 56 showing the simulation component cutting wheel during runtime.

Figure 56: Screenshot of AnyLogic state machine to explicitly address disturbances

Technical failures are triggered after a certain period of time called TBF, which is calculated

by the method calcTBF. The method applies random sampling to determine the duration

between two failures. The random sampling is based on input data in the form of probability

distributions. The generated value is assigned the variable remainDisturbance. As soon as the

corresponding state-chart enters the active state, i.e. advance in Figure 56, the value of

remainDisturbance is copied to the trigger element disturbance. The AnyLogic trigger element,

depicted as a flash, is used to trigger events after a certain period of time. The time assigned to

the trigger element can be understood as a countdown. When it comes to zero, the user-specified

Java code is executed. In the case of the trigger disturbance, the Boolean variable functioning is

set to false and the state-chart leaves the compound state operable. Furthermore, the method

calcTTR is activated. This method randomly generates a TTR and assigns it to the trigger element

repair. Since the state-chart is currently operable, the trigger is suspended, as indicated by the

colorless icon, and thereby misses the countdown. When this trigger is counted down to zero,

the fault remedy is completed and the Boolean variable functioning is set to functioning again.

Thus, the state-chart can reenter the compound state operable. The trigger repair also asks the

method calcTBF to generate a new TBF.

[109]

The general procedure of operational disturbances related to the supply chain is exemplified

in Figure 56 with the delayed delivery of grout. The production process advance must not start

without grout for backfilling the annular gap. If the next advance step can start but grout is

missing, the compound state operable is exited and the time is saved to the variable

startTime_DelayGrout. Since this disruption is not caused by the simulation component cutting

wheel but an element it depends on, the sub-state dependFailure is entered. When grout is finally

available, the compound state inoperable is exited. The current model time is subtracted from

the start time to calculate the duration of the disturbance. This duration is saved in the

histogram hist_dur_NoGrout for analysis. This general procedure of analysis is applied for all

disturbances. Other reasons can be the prolonged extension of the slurry line, a technical

disturbance in another simulation component required for advance (e.g. backfill grouting

system), and a technical failure in the cutting wheel itself.

Figure 56 also reveals that the next failure will occur in 2018.891 minutes (see trigger

disturbance). The TBF is calculated based on operating time units of the specific element. Thus,

the countdown of the cutting wheel must only be active when the state-chart is in advance. The

blue color-coding indicates that the trigger is presently counting down, as advance is currently

active. When the state advance is exited, the countdown is suspended. In order to resume the

countdown at the previous position, the variable remainDisturbance is used as temporary

memory.

The described situation is abstracted as a timeline in Figure 57 for better understanding.

Figure 57: Conceptual visualization of disturbance suspension for process inactivity

At the point in time 0, random sampling generates a TBF of 150 minutes. This value is

assigned to both the trigger disturbance and the variable remainDisturbance. The first advance

step takes 55 minutes. Thus, after advance is finished, the time remaining until a disturbance

occurs is 95 minutes. This is temporarily stored in the variable remainDisturbance. The

countdown is suspended. The subsequent ringbuild takes 30 minutes. Therefore, the timeline

progresses to 85. When advance is resumed, the countdown for disturbance is reassigned with

the value of remainDisturbance. The second advance takes 60 minutes. Therefore, the timeline

progresses to 145 and remainDisturbance is overwritten with the new value of 35. The trigger

disturbance is again suspended. It is very likely that during the next advance step, the trigger will

ultimately count down to zero and evoke a disturbance. This initiates the general procedure of

disturbance simulation, as discussed above.

[110]

8 Case Study

In the following, the developed simulation-based approach to quantify production

disturbances in mechanized tunneling is demonstrated by means of three application examples.

The presented case study is based on a metro project introduced in Chapter 6. The tunnel is

driven by a slurry shield machine.

In the first section of this chapter, the application examples are presented separately. A

comparison is given at the end. The examples are chosen to highlight the influence of the three

disturbance categories discussed in Chapter 5. In the first application example, only

disturbances of elements directly affecting the production are considered. If a failure occurs in

one of these elements, production is immediately suspended. The second example considers

problems arising from an inadequate supply chain. Thus, production is also affected if the

required amount of material or the required capacity is not deployed timely. In the last

simulation study, the impact of cascading disturbances is visualized.

In the second part of this chapter, the case study is discussed and compared to the real data.

Thus, this chapter ends with a discussion on the validation and verification of the presented

approach.

8.1 Demonstration of the developed approach by application examples

In this section, three consecutive application examples are presented and discussed to

demonstrate the presented simulation-based approach. A major advantage of simulation over

analytical methods is the possibility to apply uncertain representations like probability

distributions. This implies, however, that the execution of a single experiment is not sufficient

since it only shows one manifestation of the underlying uncertainty representation. In order to

generate sound results in simulation studies, the experiments must be conducted several times.

This eliminates the chance of misinterpretations due to the analysis of a single freak value

experiment. For the case study at hand, a Monte-Carlo experiment with 1000 simulation runs

was conducted for every simulation study. The presented durations are net values as

interruptions due to weekends, work breaks, or similar are not considered.

8.1.1 Application example 1

The first simulation study visualizes the impact of disturbances that immediately affect

production. No other modeled disturbance is considered in this example. As stated earlier,

several assembly parts of the machine must operate to perform a production process. In the

context of slurry shield machines, the relevant elements for advance include the cutting wheel,

backfill grouting system, slurry circuit, and thrust cylinders. The production process ringbuild on

the other hand is suspended immediately if a failure occurs in the erector or thrust cylinders.

[111]

For this example and the applied project data, the most influential disturbances are problems

with the cutting wheel and blockages of the slurry circuit. The geological conditions of the

reference project showed highly fractured rocks. The loosened rock chips were often too large

for the circuit and jammed the pumps. This problem alone has such severe impact that late

modifications were made to shred oversized chips before adding them to the slurry circuit.

However, as mentioned in Chapter 6, downtime for the modification works is excluded from

data analysis.

In Chapter 5, the implementation of technical disturbances in the developed simulation-based

approach is presented. In order to simulate the described disturbances, two values must be

determined: the TBF and the TTR from a disturbance. Taking their uncertain nature into account,

probability distributions are derived by the application of distribution fitting to the project data.

Consequently, two probability distributions functions are specified for all of the elements

mentioned above. The relevant parameters of these functions are given separately in Table 5.

The abbreviations LEP and UEP represent lower end point and upper end point, respectively.

During runtime, the specific values for TBF and TTR are randomly sampled from these

distributions. The density-histogram plots for applied functions identified by distribution fitting

are shown in Appendix E.

Table 4: Parameters of probability distribution functions for TBF of simulation components

stringently required for execution of production processes

Simulation Component Distribution Parameter

Cutting wheel Johnson SB α1 = 0.98; α2 = 0.44; LEP = 0.45; UEP = 9 628.02

Erector Beta α1 = 0.46; α2 = 3.10; LEP = 9.26; UEP = 54 209.85

Backfill grouting system Beta α1 = 0.79; α2 = 4.60, LEP = 42.65; UEP = 15 125.60

Slurry circuit Weibull (E) loc = 9.86; α = 0.60; β = 530.15

Thrust cylinder Beta α1 = 0.82; α2 = 5.99; LEP = 12.80; UEP = 22 108.75

Table 5: Parameters of probability distribution functions for TTR of simulation components

stringently required for execution of production processes


Cutting wheel Johnson SB α1 = 0.84; α2 = 0.49; LEP = 8.19 ; UEP = 1 338.55

Erector Pearson 6 loc = 0.00; α1 = 4.09 ; α2 = 2.04; β = 18.96

Backfill grouting system Johnson SB α1 = 2.12; α2 = 0.71; LEP = 3.54 ; UEP = 2618.23

Slurry circuit Johnson SB α1 = 1.99; α2 = 0.59; LEP = 4.98 ; UEP = 1 865.98

Thrust cylinders Johnson SB α1 = 1.26; α2 = 0.49; LEP = 0.00 ; UEP = 1 440.00

[112]

Figure 58 presents the project durations gathered from the 1000 runs and classified in a

histogram. The average project makespan for example 1 manifests at around 166.7 days. The

shape of the histogram shows a normal trend, which can be explained by the central limit

theorem that states that, if a sufficiently large sample size is chosen, the results of an experiment

with independent, random variables resembles a normal trend regardless of the underlying

distributions [Law, 2014]. The normal trend is clearly visible even though the histogram has a

few peaks and declines compared to a mathematical representation of a normal distribution

function. If the sample size is increased, the same trend is obtained and thus the number of

simulation runs can be considered sufficiently large.

Figure 58: Total makespan of 1000 simulation runs gathered with application example 1

Figure 59 depicts the averaged time shares from the Monte-Carlo experiment. The presented

values represent the average durations of advance, ringbuild, and downtime from the 1000

simulation runs. Accordingly, the total average duration of advance is 58.1 days. The time taken

to build the 667 rings adds up to total duration of 24.7 days and the project was delayed by 83.9

days because of disturbances related to elements required for the core processes.

Figure 59: Distribution of average durations in application example 1

0

10

20

30

40

50

60

135 140 145 150 155 160 165 170 175 180 185 190 195 200Nu

mb

er

of

occ

ure

nce

s w

ith

in

10

00

sim

ula

tio

n r

un

s

Total project duration in days

Ringbuild 24.7 days

Advance 58.1 days

Downtime (prod. processes) 83.9 days

[113]


The second example presented at this point goes beyond the first in terms of disruptions due

to an insufficiently dimensioned supply chain. In the first example, resources were available at

all times and in unlimited quantities. The second example, however, explicitly considers the

transportation and handling of required materials. Disruptions of the production thus also arise

from delayed material deliveries. The reasons for these delays are either technical failures of the

corresponding elements, e.g. crane or vehicle, or insufficiencies in the supply chain. Because

support processes are performed in parallel to production, disturbances may or may not cause

downtime. Production is only affected if the required material is not available at the moment

when the process execution is scheduled to start.

In general, the simulation of these disturbances is similar to the previous example. They are

modeled by the application of probability distributions for TBF and TTR. The relevant elements

for this disturbance pattern are: gantry crane, grout pump, segment crane, and vehicle. The

specific parameters of the corresponding probability distributions are presented in Tables 6 and

7 respectively.

Table 6: Parameters of probability distribution functions for TBF of simulation components of

the supply chain


Gantry crane Triangular min = 2 400; mean = 13 211; max = 240 000

Grout pump Beta α1 = 0.79; α2 = 4.60 LEP = 42.65; UEP = 15125.60

Segment crane Triangular min = 1 800 ; mean = 6 055 ; max = 8 000

Vehicle Johnson SB α1 = 1.46; α2 = 0.47; LEP = 4.37; UEP = 37 088.03

Table 7: Parameters of probability distribution functions of TTR of simulation components of the

supply chain


Gantry crane Triangular min = 10; mean = 60; max = 240

Grout pump Johnson SB α1 = 2.12; α2 = 0.71; LEP = 3.54; UEP = 2618.23

Segment crane Triangular min = 360; mean = 600; max = 1 440

Vehicle Beta α1 = 0.70; α2 = 5.74; LEP = 9.69; UEP = 318.84

Apart from trains arriving too late, a typical example of disruptions related to the supply

chain that is frequently observed in the reference project is the extension of slurry pipes: This

task is performed parallel to ringbuild but frequently exceeds the duration of this production

process. In principle, the next advance step can start when the ring is assembled completely. But

[114]

if the extension of slurry pipes is not finished, the next advance cycle must be delayed as

mucking is crucial. Consequently, the duration of pipe extension exceeding the duration of

ringbuild is considered a disturbance. However, the extension of the slurry pipes is in fact a

support process and not a disturbance as such. Consequently, the representing probability

distribution function is listed not in the table above but in Chapter 6.3.2. It is a regular process

that is required after every four advance steps. The duration of this process may exceed the

duration of ringbuild and thus delay the next advance step. This issue clearly describes a reason

for downtime that requires the detailed analysis of the interaction between production and

logistic processes.

Again, 1000 simulation runs were performed for this example, the results of which are given

in Figure 60. As expected, the total makespan increased significantly. The mean makespan for

the second simulation study is 191.3 days. The mean duration of the two production processes

did not change significantly. The rather large set of simulation experiments, on which the

calculation of the mean duration is based, levels the irregularities and provides stable results.

The same is true for downtimes due to failures of production related elements. In addition to the

83.9 days of downtime due to technical failure of main elements, 24.6 days of downtime are

attributed to disturbances originating in the supply chain. The comparably small influence of an

insufficiently dimensioned supply chain can be explained by the rather short tunneling length

and the slow excavation speed. This combination provides a lot of buffer time for material

deliveries, even when the processes are slow or disturbed. The biggest share of this disturbance

category is thus related to the extension of slurry pipes, which alone provoked a downtime of

roughly 12 days.

Figure 60: Distribution of average durations in application example 2


In this third and final application example, the effects of cascading disturbances are

demonstrated. The modeling of this disturbance category requires a thorough system analysis in

order to determine the relevant elements and process interactions and model them accordingly.

The presented approach exemplifies the concept by the cascading disturbance caused by the

limited processing time of grout, defined as 14 hours. If the time between grout mixing and

consumption exceeds these 14 hours, the old grout must be disposed of to prevent damages to

Ringbuild 24.7 days

Advance 58.1 days

Downtime 108.5 days

Downtime (support processes)24.6 days


[115]

the backfill grouting system. The additional time to prepare new grout, rinse the system, and

handle the fresh grout is considered a cascading disturbance. The reasons for exceeding the

processing time are issues demonstrated in the previous two application examples.

A third Monte-Carlo experiment was performed, again with 1000 simulation runs. As

expected, the total makespan increased further. The mean project duration increased to 200.9

days. The downtime due to the described cascading disturbance is a total of 9.6 days. The mean

duration of the production processes advance and ringbuild remains unchanged. Figure 61

displays the durations of advance, ringbuild, and the three disturbance categories, averaged over

the 1000 simulation runs.

Figure 61 Distribution of average durations in application example 3

8.2 Discussion of the simulation study

The three defined disturbance patterns are visualized separately in three application

examples. In this section, the application examples are compared and discussed. This is followed

by the evaluation of the simulation study in terms of the underlying project data.

8.2.1 Comparison of the application examples

The three disturbance patterns defined in Chapter 5 are visualized separately in three

application examples. A comparison of the generated results in a column chart is provided in

Figure 62. The presented durations represent the mean values generated in the distinct Monte-

Carlo experiments with 1000 repetitions.

Figure 62 shows that the results simulated for the production processes advance and

ringbuild remain unchanged throughout the simulation study. This can be explained by the

comparatively high number of repetitions. The combination of 667 advance cycles for each

simulation run and 1000 simulation runs for each Monte-Carlo experiment eliminates stochastic

irregularities and provides stable results.

Ringbuild 24.7 days

Advance 58.1 days Downtime

118.1 days


Downtime (support processes)24.6 days

Downtime (casc. disturbances) 9.6 days

[116]

Figure 62: Comparison of mean results generated in the three application examples

The first category of disturbances relates to elements stringently required for the production

processes. This disturbance pattern is demonstrated in the first simulation study, which features

83.9 days of downtime. In general, this disturbance category is difficult to influence. The failure

rate might be influenced by a proactive maintenance regime to a certain extent. Furthermore,

the duration for troubleshooting could be minimized by training employers and stockpiling most

spare parts. But nevertheless, these disturbances cannot be totally prevented, and this is anyway

not the intention of the presented approach. However, the simulation-based approach presented

in this thesis provides the possibility to take these disturbances into account considering their

uncertain nature. Thus, reliable and more robust project scheduling is possible.

Downtimes attributed to the disturbances of the supply chain are visualized in the second

application example. These disturbances account for a total downtime of 24.6 days. A major

share of downtime can be related to the extension of slurry pipes, which delayed the start of the

next production cycle. Almost 12 days were lost because the extension of slurry pipes exceeded

the duration of ringbuild. Training of the crew could possibly reduce the high amount of

downtime and thus be a countermeasure for project executives. The rest of the 24.6 days were

lost due to delayed material delivery. The use of another train could reduce this duration

somewhat but the benefits must be compared to the additional costs and effort.

The last disturbance pattern, the cascading disturbances presented within this thesis, is

highlighted in the last simulation study. Cascading disturbances are exemplified by the

exceeding of processing time of the grouting due to prolonged, preceding disturbances hindering

grout consumption. The time losses in the third application example are due to the disposal of

the hardened grout and delivery of fresh grout. The downtime originating from cascading

disturbances accounts for a total of 9.6 days. The simulated results show that the application of a

two-component grout might be promising. However, two aspects should be considered during

decision-making. The use of two-component grout requires the installation of an additional set

58.1 58.1 58.1

24.7 24.7 24.7

83.9 83.9 83.9

24.6 24.6

9.6

0

50

100

150

200

250

App. example 1 App. example 2 App. example 3

Pro

ject

du

rati

on

in

da

ys

Advance RingbuildDowntime (prod. processes) Downtime (support processes)Downtime (casc. disturbances)

[117]

of pipes to pump the basic grout to the machine. In view of the high performance losses related

to the prolonged extension of slurry pipes, this might prove disadvantageous. Also, the second

component is usually delivered to the TBM in container and lasts for several advance steps. This

implies an additional material-handling operation ever so often. On the other hand, the delivery

of grout by the vehicle for every production cycle cannot be considered. The second aspect is

related to assembly group backfill grouting system itself. The application of a two-component

grout requires several technical changes. The additional system might be prone to failure if it is

not maintained properly. Therefore, the failure rate must be updated and the simulation study

repeated when reliable project data is available.

Whether or not the system is modified according to discussed suggestions always remains an

economic question. The presented approach, however, provides a tool for project managers to

reach a decision based on transparent information instead of rough estimations with discrete

values. Additionally, if applied during the planning phase, the presented approach provides the

possibility for a holistic system investigation of operational processes.

In Table 8, the mean values and corresponding variance gathered during the 1000 simulation

runs are separately displayed for the application examples.

Table 8: Comparison of simulation results

App. example 1 App. example 2 App. example 3

mean deviation mean deviation mean deviation

Project duration 166.68 9.02 191.25 8.18 200.81 9.73

Advance 58.07 0.09 58.06 0.09 58.06 0.09

Ringbuild 24.68 0.7 24.68 0.7 24.68 0.7

Downtime 83.93 9.03 108.51 8.17 118.06 9.71

8.2.2 Comparison of simulation results with project data

Figure 63 provides a comparison of the reference project data and the results gathered in the

application example 3. The particular durations feature a good similarity. Consequently, it can be

assumed that the processed input data for the production processes is a good representation of

the monitored data. This is most probably related to the good quality and large quantity of

available observations for these processes. The similarity of simulated and observed downtime

is not as strong as for the production processes but it is still acceptable. The discrepancy can be

ascribed to the comparably small number of observations used to derive the input data. If more

project data were available, the results would be even better. In brief, it can be stated that the

developed simulation approach describes the observed system well. Investigations of upcoming

projects and evaluation of alternative supply chains setups can be made. Consequently, the

validation of the presented approach is successful.

[118]

Figure 63: Validation of the simulation approach with gathered project data

58.1 58.6

24.7 23.5

118.1 122.2

0

50

100

150

200

250

Application example 3 Reference project

Pro

ject

du

rati

on

in d

ay

s

Advance Ringbuild Downtime

[119]

9 Final remarks

The final remarks are given in this section. At first, a brief summary outlines the thesis and

the developed approach. Following that, a conclusion reviews the approach in terms of the

initially stated research goals. Finally, an outlook highlights potential enhancements of the

approach, discusses industry application, and addresses issues to be solved in future work.

9.1 Summary

The holistic consideration of disturbances in production and the identification of bottlenecks

in the supply chain are identified as vital aspects of sound project planning of mechanized

tunneling operations. A discussion on conventional planning tools highlights their value for

approximations and first estimations, but also reveals several downsides, such as lack of

transparency, limited possibilities to address uncertainty, high level of abstraction about process

interactions, and a general difficulty in considering disturbances in a detailed manner. These

deficiencies are aggravated by the uniqueness of construction processes and boundary

conditions.

In order to address these deficiencies, the thesis presents a simulation-based approach to

quantify the influence of disturbances on production performance in mechanized tunneling. The

approach Simulation is generally accepted as a valuable tool for the planning and analysis of

complex systems. A review of related literature identifies relevant characteristics. Five

simulation-based approaches considering disturbances are compared in terms of these

characteristics. The comparison shows that the area of research is not satisfactorily addressed

by others. Following that, five research goals are stated clearly (see Section 2.3), which are

evaluated in terms of fulfillment in Section 9.2. The conceptual methodology of the research is

presented in Section 2.4, which follows a structured procedure model. Figure 64 shows the basic

steps of this procedure model to summarize the presented thesis.

Figure 64: Basic steps of the procedure model followed in the presented thesis

Goals

Requirements

Project data

Input data

Formal model

Simulation model

Simulation study

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The procedure model starts by stating the desired goals. Requirements for the developed

approach are derived on the basis of these objectives. Following this, the procedure continues in

two parallel tracks: modeling and data acquisition.

The modeling starts with a thorough system analysis, as depicted in SysML notation. The

SysML notation is chosen for several reasons. It features a graphical form of presentation, has a

close resemblance to AnyLogic’s modeling of state-charts, and focuses on the description of

physical systems for computational purposes. But most importantly, the process representation

resembles a simple flowchart. The flowchart methodology is a common standard tool in

engineering and business administration, and thus usually familiar to the client and other

stakeholders.

The requirements define the scope of the system analysis. Relevant elements for production

and logistic of mechanized tunneling operations are identified and their operational aspects are

stated in SysML block definition diagrams. The various flows of material are modeled in SysML

internal block definition diagrams. The material-handling operations related to the transporting

vehicle are presented separately. The operational behavior of all relevant elements is modeled in

distinct SysML state machine diagrams. The process interactions are presented in SysML

sequence diagrams.

The key theme of the presented approach is the quantification of operational disturbances.

Consequently, the modeling of these disturbances is discussed separately in this thesis. Three

operational disturbances are classified: disturbances directly affecting production, disturbances

related to the supply chain, and cascading disturbances. Cascading disturbances may arise when

the duration of a disturbance exceeds a certain threshold. This issue is exemplified in the

presented thesis by the disposal of hardening grout to prevent damages to the backfill grouting

system.

The formal model description is used for implementation of the simulation framework

AnyLogic. The implementation is described in detail. The close resemblance between the SysML

notation and the AnyLogic simulation framework highly facilitates the verification of modeling

steps. The developed approach is based on distinct simulation components according to the

identified system elements. The modular concept ensures high reusability and flexibility and

facilitates the development of new simulation components to extend the range of application.

The communication between simulation modules is achieved by a central event manager based

on the observer-observable design pattern. AnyLogic supports the parallel application of several

simulation paradigms. Therefore, the need to abstract certain system aspects to allow modeling

in one specific simulation paradigm is eliminated. The presented approach makes use of this

possibility and uses the DES concept to simulate processes and discrete material-handling

operations and the system dynamic concept to model the continuous material flows. This multi-

method simulation approach further reduces the level of abstraction and thus provides more

transparency. Finally, the implementation of the defined disturbance patterns is presented in

detail.

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The second track of the applied procedure model relates to the acquisition of adequate

project data to validate the developed approach. The acquisition of operational project data is

discussed in Chapter 6. First, a reference project is presented. The required operational data is

exported from the work-shift reporting system used in the project. These work-shift reports

include TBM sensor data, which is automatically assessed through the PLC, and also process

information manually recorded by shift engineers or foremen. In order to apply this project data

in simulation studies and model uncertainties of the operations, probability distribution

functions are derived from the project data. This processing of project data to input data is

described separately. The statistical software application ExpertFit is applied for distribution

fitting. In this method, distributions are fitted to the underlying data set and adjusted to

represent them accordingly. Distribution fitting is performed for the required processes and to

simulate the occurrence and extent of operational disturbances.

Finally, the developed approach is demonstrated and validated in the case study of a slurry

shield machine. The distinct simulation components are combined with the corresponding the

input data, derived through distribution fitting. The case study is clustered in three application

examples to highlight the influence of the three operational disturbance patterns. The validation

of the project data shows good results and indicates that the developed approach can generally

be applied. However, it must be stated that further validation efforts are advisable to finally

assess the quality.

Validation should always be accompanied by verification. The application of a structured

procedure model facilitates the verification of the various steps. The close resemblance between

the formal SysML and AnyLogic proved valuable when comparing the two models. Other steps

were not as easy and required an iterative approach to ensure consistency with the desired

output. It should be kept in mind that every step described in the procedure model could be

affected by changes due to new information or problems arising throughout the period of model

development. The examination of inherent correctness is valuable for avoiding major changes

when verifying and validating with other steps.

In brief, this thesis provides a tool to support the planning of mechanized tunneling

operations. A simulation-based approach has been developed that holistically addresses the

interaction and dependencies between production and logistic processes influenced by

uncertainties. The consideration of uncertainties is possible. The software implementation is

based on a modular concept with distinct simulation modules. The classification and

implementation of disturbance patterns, in combination with the detailed modeling of process

interdependencies, enable the sound quantification of project downtime. This way, project

scheduling can explicitly address performance loss and provide robust, reliable plans.

Additionally, the identification of logistical bottlenecks is possible. The modular simulation

approach facilitates the process of model development and allows the transparent evaluation of

alternatives. The decision-making process in mechanized tunneling is thus supported by a

transparent planning tool.

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9.2 Conclusion

In order to draw a conclusion, the stated research goals are discussed separately, followed by

a brief resume of the presented approach.

1. Transparency to facilitate assessment by users with limited simulation experience

The research goal is addressed by the formalization of the standardized SysML notation. The

formal model provides a detailed insight into the complex domain of mechanized tunneling.

Four diagram types of the SysML standard are applied. The structural composition of the system

is modeled by SysML block definition diagrams. The flow of required material between the

various elements is described by SysML internal block diagrams. The procedural behavior of

active elements is modeled in distinct SysML state machines diagrams. The procedural

interdependencies between elements are expressed by SysML sequence diagrams.

The formalized model is implemented in the AnyLogic simulation framework. The approach

is based on distinct simulation modules that are identical to the system elements defined in the

formal model. The behavioral aspects are implemented in AnyLogic’s state-chart concept. This is

almost identical to the specified SysML state machines diagrams.

Finally, a graphical modeling representation is holistically applied in the approach. The

process representation is similar to the flow chart notation. Flow charts are a common

representation format in engineering and business administration; they enable all stakeholders

to evaluate the presented approach. Thus, a very high level of transparency is achieved. This

might finally improve the ongoing concerns against simulation as a planning tool in the

construction industry.

2. Consideration of uncertainties resulting from operational data and geotechnical conditions

The thesis especially addresses uncertainties in operational data; the main focus is put on the

evaluation of disturbances. For this purpose, the work-shift documentation of a tunneling

project was analyzed, structured, and processed with distribution fitting methods to derive

adequate input data. This operational input data is provided in the form of probability

distribution functions that are used in the simulation framework for random sampling. The

validation of the simulated results with the project data shows the general applicability of the

presented approach.

Uncertainties related to geotechnical conditions are not addressed explicitly in this thesis as

the reference project is constructed in a homogenous soil formation. However, this issue has

been addressed during research and published by Rahm et al. [2012] and [2013].

3. Consideration of interdependencies between production and logistic

The formalization and implementation explicitly address both production and logistic

processes. The interdependencies are modeled on the one hand by required materials or

capacities to execute (start) certain processes. On the other hand, SysML sequence diagrams are

used to formalize the exchange of information in the system. In order to provide flexibility and

[123]

enable modularization, the observer-observable design pattern is applied for implementation in

AnyLogic. This design pattern streamlines the communication between the simulation

components into a central event manager. This way, hard structures connecting the particular

simulation components can be avoided. This highly supports the flexible exchange of

components to define various setups.

4. Consideration of disturbances and their influence on dependent and successive processes

Disturbances affecting the production in mechanized tunneling were classified into three

general categories: disturbances directly affecting production, disturbances related to the supply

chain, and cascading disturbances. Disturbances directly affecting production are basically

expressed in the behavior of system elements, i.e. in SysML state machines diagrams and state

machines diagrams, respectively. Disturbances related to the supply chain are the result of the

interdependencies of production and logistic processes, and the availability of required

materials or capacities. Cascading disturbances arise if a disturbance exceeds a certain threshold

and dependent processes are affected by that. A very high level of detail is required to model

such interdependencies.

5. Reusability to limit modeling effort and evaluate possible project alternative

To ensure flexibility of the approach, distinct simulation components were developed

according to the structural analysis provided by the SysML model. The observer-observable

design pattern enables communication between these elements. The configuration of alternative

setups is possible. Especially, for EPB shield machines the mucking logistic is often a critical

bottleneck. The comparison of continuous mucking by installation of tunnel belt conveyor or

discontinuous, cart-based mucking is generally possible. However, it is not part of the presented

thesis as reliable input data for validation could not be obtained by the author. This issue should

be addressed in further research efforts.

In conclusion, it can be stated that the postulated research goals were addressed successfully.

The necessary transparency to allow independent assessment is achieved through the alignment

of modeling approaches. The approach holistically covers all performance-influencing aspects of

mechanized tunneling operations. Still, the level of detail is high enough to account for

interdependencies and explicitly consider disturbances. The consideration of uncertainties is

possible for both operational data and the influences of geotechnical conditions. The approach is

validated with project data and shows general applicability. The concept of distinct simulation

components provides flexibility and keeps the effort for model development low. Furthermore,

the modularization provides a good foundation for further efforts. Additional simulation

components can be developed easily to extend or exchange existing components or adapt to new

requirements.

[124]

9.3 Outlook

The simulation-based approach to quantify the processes disturbances presented in this

thesis has proved strong in terms of general applicability. The initially postulated research goals

are effectively addressed and validation with project data is satisfying. A drawback of the

presented research is the limited source of project data to validate the approach. The approach

is generally suitable for application in planning purposes in order to acquire more operational

project data.

Naturally, this will provoke several new requirements from users and identify additional use

cases. These use cases are valuable fields for future research. The presented approach follows a

modular concept of distinct simulation components. This facilitates the enhancement of the

simulation components presented within this thesis, as well as the implementation of new

components to address these new use cases. In the works of Scheffer et al. [2014a], Scheffer et al.

[2014b], König et al. [2014], and Scheffer et al. [2016], the approach presented in this is thesis is

enhanced to consider the logistical aspects of the surface jobsite facilities. The presented thesis

addresses this domain of mechanized tunneling only in an abstracted manner. This

enhancement is a valuable step toward further increase in the holism of this simulation-based

approach.

In the author’s opinion, another very promising field of research is provided by the

integration of detailed maintenance operations. The performance-decreasing effect of wear and

tear is completely neglected in the presented approach. In order to account for performance

reduction and, in the worst case, failure of elements due to prolonged maintenance intervals, the

cause and effect of wear and tear must be analyzed and modeled in detail. The wear and tear of

the cutting tools provides the most prominent and cost-intensive maintenance effort in

mechanized tunneling. Implementing the relation between prevailing soil conditions, condition

of mounted tools, and operational parameters, e.g. pressure at face, in a performance related

simulation framework will be a challenging task. The first approach to address this problem in

the presented approach can be found in the work of Scheffer et al. [2015].

Rahm et al. [2012] and [2013] introduce the possibility to consider uncertain soil conditions

in the presented approach. This could be coupled with surrogate modeling to predict upcoming

soil formations and this information could be used for updated simulation studies during project

execution. This will extend the applicability of the presented approach from the planning phase

to the execution phase. In the same context, a connection between the presented approach and

sophisticated work shift reporting applications (see for example the work of Mayer et al. [2014]

or Nagel [2015]) should be investigated. The operational input data for the presented simulation

study was derived from such an application. The structuring and processing by distribution

fitting methods could be automated to update the planning. If both operational data and

geological conditions are updated constantly during project execution, the gathered quality of

gathered results will improve significantly. Additionally, this procedure will provide a solid

[125]

knowledge base for the planning of upcoming projects. The availability of structured and reliable

data is the backbone of any sound simulation study.

Finally, the recent developments in the area of building information modeling (BIM) might

provide further applications to improve the presented approach. A general use case of the BIM

methodology is the comparison between planned and as-built data of buildings. The results of

simulation studies gathered with the presented approach before project realization can be

evaluated against the data experienced during project execution. This comparison will further

enhance the reliability of the aforementioned knowledge base to plan future projects.

[IX]

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[XXIII]

Appendix A – SysML block definition diagrams

Figure 65: SysML bdd for mucking device in tunnel domain

Figure 66: SysML bdd to show the generalization of cargo items

properties

cur flow rate : VolFlowmax rate : VolFlowextension length : Length

properties

cur extraction rate : Ratemax rate : Rate

«block»Slurry Circuit

«block»Tunnel Belt Conveyor

flow specification

inout slurry : Slurryinout bentonite : Bentonite

flow specification

inout muck : Muck

bdd Tunnel pipelines [Generalization of pipelines in tunnel domain]

«block»Grout Pipe

flow specification

inout grout : Grout

«block»

Tunnel Pipeline

bdd Cargo [Generalization of cargo items]

properties

id : ID length : Length width : Length height : Length weight : Weight

«block»Cargo

«cargo»Container

properties

content : Liquidcurrent stock : Volume

«cargo»Segment

properties

type : Text

[XXIV]

Figure 67: SysML bdd to show the generalization of relevant liquids

Figure 68: SysML bdd to show the generalization of storages in the surface facility domain

«block»Liquid

bdd Liquid [Generalization of relevant liquids]

«liquid»Bentonite

«liquid»Muck

«liquid»Slurry

«liquid»Hardener

properties

density : Density

«liquid»Grout

properties

processing time : Time

«block»Muck Pit

«block»Grout Batching Plant

«block»Segment Storage

bdd Hierarchical decomposition [Generalization of storages in surface facility domain]

properties

id : ID locationX : ReallocationY : Reallength : Distance width : Distanceheight : Distance

«block»

Storage

properties

cur inflow rate : VolFlowtotal capacity : Volumecurrent stock : Volume

flow specification

inout muck : Muck

properties

max rate : VolFlowtotal capacity : Volumecurrent stock : Volumeprepartion time : Time

flow specification

inout grout : Grout

properties

batch size : Integertotal capacity : Integercurrent stock : List[Segment]

flow specification


«block»Auxiliary Storage

flow specification

inout hardener : Hardener

«block»Bentonite Storage

properties

cur inflow rate : VolFlowcur outflow rate : VolFlowtotal capacity : Volumecurrent stock : Volume

flow specification

inout bentonite : Bentonite

properties

max rate : VolFlowcurrent stock : Volume

[XXV]

Appendix B – SysML internal block definition diagrams

Figure 69: SysML ibd of material flow in 2-component grouting

Figure 70: SysML ibd of segment transport for a setup with a segment transfer table

groutPlant: Grout Batching Plant

groutPipe: Grout Pipe

bis : Backfill Grouting System

ibd MaterialFlow [Flow of materials in two-component grouting]

GroutGrout

Hardener

Grout

auxStorage: Auxiliary Storage

hTank: Comp B Temp Storage

Hardener Hardener

hContainer : Cargo



Hardener

Grout

sStorage : Segment Storage

crane1: Gantry Crane

train1: Vehicle

sCrane : Segment Crane

segTable: Segment Table

ibd Material flow [Flow of segments with segment transfer table]

SegmentSegment

Segment

Segment

erector : Erector

Segment

r : Ring

Segment


Segment

[XXVI]

Appendix C – SysML state machine diagrams

Figure 71: SysML stm for backup belt conveyor

Figure 72: SysML stm for tunnel belt conveyor

Figure 73: SysML stm for grout pump

idle

mucking

awaitingvehicle

stm Process description [Processes executed by backup belt conveyor]

MuckCapac

MuckStart MuckFin

[muck cart full]

[muck cart available]

NoMuckCapac

[muck cart

available]

MuckStart

idle

mucking

MuckFin

stm Process description [Processes executed by tunnel belt conveyor]

operable

inoperable

[extension required]

H*[extension required]

idle

stm Process description [Processes executed by grout pump]

activeDeliveyG[grout tank empty]

UnloadGFin

UnloadGStart

GAvailable

[grout cart empty]

[XXVII]

Figure 74: SysML stm for grout circuit – component A

idle

active

AdvFin

AdvStart

stm Process description [Processes executed by grout component A feeding line]

operable

inoperable

[extension finished]

H*

[extension finished]

[XXVIII]

Appendix D – SysML sequence diagrams

Figure 75: SysML sd showing the interaction during advance with EPB shield machines

Figure 76: SysML sd showing the interaction during material handling of grout

sd Process interaction [Interaction during advance in EPB shield machines]

ENV :Cutting Wheel :Thurst Cylinder :Screw Conveyor:Backfill

Grouting System

RbFin

AdvStart

AdvStart

AdvStart

StrokeComp

AdvFin

AdvFin

AdvFin

AdvFin

[all components operable AND tunnellength != complete]opt

sd Process interaction [Interaction for material handling operations for grouting]

ENV:Backfill

Grouting System :Vehicel

AdvStart

UnloadGFin

DeliveryG

:Grout Pump

UnloadGStart

GroutAvail

[grout available]opt

AdvFin

[XXIX]

Figure 77: SysML sd showing the interaction of segment handling with a segment feeder

Figure 78: SysML sd showing the interaction during material handling at jobsite facility

sd Process interaction [Interaction for material handling operations with segment feeder]

ENV :Erector :Segment Feeder :Segment Crane :Vehicle

AdvFin

RbFin

SegCraneRelease

[first position empty]

UnloadSFin

DeliveryS

UnloadSStart

loop

StorageCapac

[first position full]loop

SegRemove

SegAvail

sd Process interaction [Element interaction for material handling operation at surface jobsite facility]

:Vehicle

LoadStart

:Gantry Crane

[load equals order]loop

LoadFin

[cart-based mucking]optDeliveryM

UnLoadMStart

UnLoadMFin

[XXX]

Figure 79: SysML sd showing the interaction during material of spoil in cart-based mucking

sd Process interaction [Interaction for cart-based muck handling]

ENV :Cutting Wheel:Backup Belt

Conveyor

RbFin

MuckingCapac

:Screw Conveyor

opt

AdvStart

:Vehicel

MuckStart

MuckStart

MuckFin

MuckFin

MuckingCapac

MuckStart

MuckStart

[mucking capacity available]

StrokeComp

MuckFin

MuckFin

AdvFin

[! mucking capacity available]opt

AdvFin

[XXXI]

Appendix E – Distribution fitting results

Figure 80: Density-histogram plot for advance rate

Figure 81: Density-histogram plot for segment installation

24 intervals of w idth 2 1 - Weibull

0,00

0,02

0,04

0,06

0,08

0,10

0,12

4.00 10.00 16.00 22.00 28.00 34.00 40.00 46.00

Density

/Pro

port

ion

Density-Histogram Plot - Adavance rate Density-Histogram Plot - Adavance rate

Interval Midpoint

26 intervals of w idth 1.5 1 - Log-Logistic

0,00

0,05

0,10

0,15

0,20

0,25

0,30

0,35

2.55 8.55 14.55 20.55 26.55 32.55 38.55

Density

/Pro

port

ion

Density-Histogram Plot - Segment InstallationDensity-Histogram Plot - Segment Installation

Interval Midpoint

[XXXII]

Figure 82: Density-histogram plot for pipe extension

40 intervals of w idth 25 1 - Log-Logistic

0,00

0,05

0,10

0,15

0,20

0,25

22.50 147.50 272.50 397.50 522.50 647.50 772.50 897.50

Density

/Pro

port

ion

Density-Histogram Plot - Pipe ExtensionDensity-Histogram Plot - Pipe Extension

Interval Midpoint

[XXXIII]

Figure 83: Density-histogram plot for TBF of erector

Figure 84: Density-histogram plot for TTR of erector

28 intervals of w idth 1,000 1 - Beta

0,00

0,50

1,00

1,50

2,00

515.00 4,515.00 8,515.00 12,515.00 16,515.00 20,515.00 24,515.00

Density

/Pro

port

ion

Density-Histogram Plot - Erector - TBFDensity-Histogram Plot - Erector - TBF

Interval Midpoint

12 intervals of w idth 25 1 - Pearson Type VI(E)

0,00

0,10

0,20

0,30

0,40

17.50 67.50 117.50 167.50 217.50 267.50

Density

/Pro

port

ion

Density-Histogram Plot - Erector - TTRDensity-Histogram Plot - Erector - TTR

Interval Midpoint

[XXXIV]

Figure 85: Density-histogram plot TBF of cutting wheel

Figure 86: Density-histogram plot TTR of cutting wheel

25 intervals of w idth 350 1 - Johnson SB

0,00

0,20

0,40

0,60

0,80

180.00 1,580.00 2,980.00 4,380.00 5,780.00 7,180.00 8,580.00

Density

/Pro

port

ion

Density-Histogram Plot - Cutting Wheel - TBFDensity-Histogram Plot - Cutting Wheel - TBF

Interval Midpoint


0,00

0,10

0,20

0,30

0,40

0,50

0,60

60.00 260.00 460.00 660.00 860.00 1,060.00 1,260.00 1,460.00

Density

/Pro

port

ion

Density-Histogram Plot - Cutting Wheel - TTRDensity-Histogram Plot - Cutting Wheel - TTR

Interval Midpoint

[XXXV]

Figure 87: Density-histogram plot TBF of slurry circuit

Figure 88: Density-histogram plot for TTR of slurry circuit

39 intervals of w idth 150 1 - Weibull(E)

0,00

1,00

2,00

3,00

4,00

85.00 835.00 1,585.00 2,335.00 3,085.00 3,835.00 4,585.00 5,335.00

Density

/Pro

port

ion

Density-Histogram Plot - Slurry Ciurcit - TBFDensity-Histogram Plot - Slurry Circuit - TBF

Interval Midpoint


0,00

0,25

0,50

0,75

1,00

42.50 342.50 642.50 942.50 1,242.50 1,542.50 1,842.50

Density

/Pro

port

ion

Density-Histogram Plot - Slurry Circuit - TTRDensity-Histogram Plot - Slurry Circuit - TTR

Interval Midpoint

[XXXVI]

Figure 89: Density-histogram plot for TBF of backfill grouting unit

Figure 90: Density-histogram plot for TTR of backfill grouting unit

24 intervals of w idth 350 1 - Beta

0,00

0,05

0,10

0,15

0,20

0,25

0,30

225.00 1,275.00 2,325.00 3,375.00 4,425.00 5,475.00 6,525.00 7,575.00

Density

/Pro

port

ion

Density-Histogram Plot - Backfill Grouting System - TBFDensity-Histogram Plot - Backfill Grouting System - TBF

Interval Midpoint


0,00

0,05

0,10

0,15

0,20

0,25

30.00 230.00 430.00 630.00 830.00 1,030.00 1,230.00

Density

/Pro

port

ion

Density-Histogram Plot - Backfill Grouting System - TTRDensity-Histogram Plot - Backfill Grouting System - TTR

Interval Midpoint

[XXXVII]

Figure 91: Density-histogram for TBF of thrust cylinders

Figure 92: Density-histogram plot for TTR of thrust cylinders


0,00

0,10

0,20

0,30

0,40

265.00 1,765.00 3,265.00 4,765.00 6,265.00 7,765.00 9,265.00 10,765.00

Density

/Pro

port

ion

Density-Histogram Plot - Thrust Cylinder - TBFDensity-Histogram Plot - Thrust Cylinder - TBF

Interval Midpoint


0,00

0,10

0,20

0,30

0,40

0,50

0,60

30.00 230.00 430.00 630.00 830.00 1,030.00 1,230.00 1,430.00

Density

/Pro

port

ion

Density-Histogram Plot - Thrust Cylinder - TTRDensity-Histogram Plot - Thrust Cylinder - TTR

Interval Midpoint

[XXXVIII]

Figure 93: Density-histogram plot for TBF of vehicle

Figure 94: Density-histogram plot for TTR of vehicle

16 intervals of w idth 1,500 1 - Johnson SB

0,00

0,25

0,50

0,75

1,00

1,25

1,50

780.00 3,780.00 6,780.00 9,780.00 12,780.00 15,780.00 18,780.00 21,780.00

Density

/Pro

port

ion

Density-Histogram Plot - Vehicle - TBFDensity-Histogram Plot - Vehicle - TBF

Interval Midpoint


0,00

0,10

0,20

0,30

0,40

0,50

0,60

15.00 45.00 75.00 105.00 135.00 165.00

Density

/Pro

port

ion

Density-Histogram Plot - Vehicle - TTRDensity-Histogram Plot - Vehicle - TTR

Interval Midpoint

[XXXIX]

Acknowledgement

The author would like to acknowledge the financial support for his research received from

the German Science Foundation (DFG) within the framework of the Collaborative Research

Center SFB 837.

[XL]

Curriculum vitae

TOBIAS RAHM Date of birth: October,21st, 1982 Place of birth: Selb, Germany Family status: married Email: [email protected]

ACADEMIC VITA

05/2011 – 12/2014 Research Assistant, Ruhr-Universität Bochum, Germany

Chair of computing in engineering Project: SFB 837 – C3 Teaching: Simulation technique, operations research

10/2008 – 05/2011 Master of Science, Bauhaus Universität Weimar, Germany

Management for construction, real estate and infrastructure Thesis: Simulation of core processes of an earth pressure balanced shield ma-chine

10/2004 – 09/2008 Bachelor of Science, Bauhaus Universität Weimar, Germany

Management for construction, real estate and infrastructure Thesis: Investigation of underride guarding systems mounted to crash barri-ers

04/2004 – 09/2004 Otto-Friedrich-Universität Bamberg, Germany

Sociology

PUBLICATIONS

PAPERS IN JOURNALS

Rahm, T., Scheffer, M., Duhme, R.J., Thewes, M. and König, M. (2016): Evaluation of Disturbances in Mechanized Tunneling using Process Simulation, Computer-Aided Civil and Infrastructure Engineering. Vol. 31(3), pp. 176–192. doi: 10.1111/mice.12143.

Scheffer, M., Rahm, T., König, M. and Thewes, M. (2016): Simulation-based analysis of integrated production and jobsite logistics in mechanized tunneling, Journal of Computing in Civil Engi-neering, pp. C4016002. doi: 10.1061/(ASCE)CP.1943-5487.0000584.

König, M., Thewes, M., Rahm, T., Scheffer, M., Sadri, K. and Conrads, A. (2014): Prozesssimulation von maschinellen Tunnelvortrieben – Verfügbarkeitsanalysen der Leistungsprozesse unter Berücksichtigung von Stillständen - Operational process simulation of mechanized tunneling – Analysis of production performances influenced by disturbances, Bauingenieur. Vol. 11, pp. 467–477.

Scheffer, M., Rahm, T. and König, M. (2014): Simulation-based analysis of surface jobsite logistics in mechanized tunneling, Computing in Civil and Building Engineering, pp. 705–712. doi: 10.1061/9780784413616.088.

[XLI]

PAPERS IN CONFERENCE PROCEEDINGS

Scheffer, M., Conrads, A., Rahm, T., Duhme, R.J., Thewes, M. and König, M. (2015): Simulation-based TBM performance prediction. In: Proceedings of the ITA World Tunnel Congress 2015, pp. 208–216. Zagreb, Croatia. ITA-AITES.

Duhme, R.J., Rahm, T., Thewes, M. and Scheffer, M. (2015): A review of planning methods for logistic in TBM tunnelling. In: Proceedings of the ITA World Tunnel Congress 2015, pp. 312–320. Zagreb, Croatia. ITA-AITES.

Scheffer, M., Rahm, T., Duhme, R., Thewes, M. and Konig, M. (2014): Jobsite logistic simulation in mechanized tunneling. In: Proceedings of the 2014 Winter Simulation Conference, pp. 1843–1854. Savannah, USA. IEEE. doi: 10.1109/WSC.2014.7020032.

Duhme, R.J., Rahm, T., Scheffer, M., König, M. and Thewes, M. (2014): Process simulation as a tool for TBM jobsite logistics planning. In: Proceedings of the 8th International Symposium on Geotechnical Aspects of Underground Construction in Soft Ground, pp. 381–386. Seoul, Ko-rea. doi: 10.1201/b17240-70.

Rahm, T., Duhme, R.J., Sadri, K., Thewes, M. and König, M. (2013): Uncertainty modeling and sim-ulation of tool wear in mechanized tunneling. In: Proceedings of the 2013 Winter Simulation Conference, pp. 3121–3132. Washington DC, USA. IEEE. doi: 10.1109/WSC.2013.6721679.

Duhme, R.J., Rahm, T., Sadri, K., Thewes, M. and König, M. (2013): TBM performance prediction by process simulation. In: Proceedings of the 3rd International Conference on Computational Methods in Tunneling and Subsurface Engineering, pp. 323–334. Bochum, Germany. EURO-TUN.

Westphal, M. and Rahm, T. (2013): Methoden zur automatischen Modelltransformation für die Simulation des maschinellen Tunnelvortriebs. In: Tagungsband des 25. Forum Bauinformatik, pp. 336–350. Munich, Germany.

Scheffer, M. and Rahm, T. (2013): Simulation der oberirdischen Baustellenlogistik beim maschi-nellen Tunnelvortrieb. In: Tagungsband des 25. Forum Bauinformatik, pp. 323–336. Munich, Germany.

Rahm, T., Sadri, K., Koch, C., Thewes, M. and König, M. (2012): Advancement simulation of tunnel boring machines. In: Proceedings of the 2012 Winter Simulation Conference, pp. 1–12. Ber-lin, Germany. IEEE. doi: 10.1109/WSC.2012.6465205.

Rahm, T., Sadri, K., Thewes, M. and König, M. (2012): Multi-method simulation of the excavation process in mechanized tunneling. In: Proceedings of the 19th International EG-ICE Workshop, pp. 1–10. Munich/Herrsching, Germany. EG-ICE.

Rudi, E., Walter, A.-C. and Rahm, T. (2012): Simulation der Personenströme innerhalb der RUB-Mensa. In: Tagungsband des 24. Forum Bauinformatik, pp. 71–78. Bochum, Germany.

Sadri, K., Rahm, T., Duhme, R.J., König, M. and Thewes, M. (2012): Process simulation as an effi-cient tool for the planning of mechanized tunneling logistics. In: Proceedings of International Symposium on Tunnelling and Underground Space Construction for Sustainable Develop-ment, pp. 130–133. Seoul, USA.

Sadri, K., Rahm, T., Thewes, M. and König, M. (2012): Prozesssimulation von maschinellen Tunnelvortrieben. In: 5. Fachtagung Baumaschinentechnik, pp. 552–570. Dresden, Germany. FVB.

[XLII]

Rahm, T. (2011): Process simulation of earth pressure balanced shield machines. In: Proceedings of the 24. Forum Bauinformatik, pp. 184–191. Cork, Ireland.

Rahm, T. and Voigtmann, J. (2010): Simulationsgestützte Untersuchung der gegenseitigen Beein-flussung der Verkehrsströme beim Bau eines Infrastrukturprojektes. In: Tagungsband des 22. Forum Bauinformatik, pp. 285–293. Berlin, Germany.

EDITOR OF CONFERENCE PROCEEDINGS

Hegemann, F., Kropp, C., Rahm, T. and Szczesny, K. (2012): Tagungsband des 24. Forum Bauin-formatik. Bochum, Germany. Europäischer Universitätsverlag. ISBN: 3899665120.

simulation-based evaluation of disturbances of production

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