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
RUHR-UNIVERSITÄT BOCHUM
SECOND REVIEWER: PROF. DR.-ING. MARKUS THEWES
INSTITUTE FOR TUNNELLING AND CONSTRUCTION MANAGEMENT
RUHR-UNIVERSITÄT BOCHUM
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 General introduction ................................................................................................................................... 46
3.2.1 System Modeling Language ...................................................................................................................... 47
3.3 Input data for simulation models ........................................................................................................ 50
3.3.1 General introduction ................................................................................................................................... 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
8.1.2 Application example 2 ............................................................................................................................. 113
8.1.3 Application example 3 ............................................................................................................................. 114
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
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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
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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.
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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
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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.
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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.
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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.
3.2.1 General introduction
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.
3.3.1 General introduction
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
inout segment : Segment
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>>
Mechanized Tunneling
<<system>>EPB 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>>Muck Pit
<<block>>Vehicle
<<block>>Grout Tank
<<block>>Grout Pump
<<block>>Segment Crane
<<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>>
Mechanized Tunneling
<<system>>EPB TBM
<<system>>Backup System
<<domain>>Tunnel
<<block>>Cutting Wheel
<<block>>Erector
<<block>>Slurry Pump
<<block>>Backfill Grouting
System
<<block>>Tank – Comp B
<<block>>Grout Comp A Feeding Line
<<block>>Segment Crane
<<block>>Segment Feeder
<<block>>Ring
<<block>>Segment
<<block>>Backup Belt
Conveyor
<<block>>Grout Line
<<block>>Tunnel Belt
Conveyor
<<block>>Thrust Cylinder
<<block>>Excavation Chamber
<<domain>>Surface Facility
<<block>>Shaft
<<block>>Grout Batching
Plant
<<block>>Segment Storage
<<block>>Gantry Crane
<<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>>Segment Crane
<<block>>Track
<<block>>Switch
<<block>>Shaft
<<block>>Gantry Crane
<<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>>Segment Feeder
<<block>>Vehicle
<<block>>Segment Storage
<<block>>Muck Pit
<<domain>>Surface Facility
<<block>>Excavation Chamber
<<block>>Temp Storage
Muck
<<block>>Grout Batching Plant
<<block>>Auxiliary Storage
SegmentGrout Hardener
Muck
HardenerGrout
Segment <<block>>Gantry Crane
<<system>>Backup System
<<block>>Grout Pump
<<block>>Segment Crane
<<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
<<external>> Surface Domain
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
<<external>> Surface Domain
<<external>> Surface Domain
Muck
Muck Muck
Muck
Slurry Slurry Slurry
BentoniteBentoniteBentonite
<<external>> Subterranean Domain
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]
chamber : Excavation Chamber
wheel : Cutting Wheel
bConveyor: Tunnel Belt Conveyor
sConveyor: Screw Conveyor
pit : Muck Pit
MuckMuck
MuckMuck
Muck
<<external>> Surface Domain
<<external>> Subterranean Domain
Muck
ibd Material flow [Cart-based mucking for EPB shield machines]
chamber : Excavation Chamber
wheel : Cutting Wheel
sConveyor: Screw Conveyor
pit : Muck Pit
Muck
Muck Muck
MuckMuck
dropOff: Backup Belt Conveyor
muckCart: Cargo
Muck
<<external>> Surface Domain
<<external>> Subterranean Domain
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
Simulation Component Distribution Parameter
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]
8.1.2 Application example 2
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
Simulation Component Distribution Parameter
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
Simulation Component Distribution Parameter
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
8.1.3 Application example 3
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
Downtime (prod. processes) 83.9 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 (prod. processes) 83.9 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.
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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
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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
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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.
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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
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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
inout segment : Segment
«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
<<external>> Subterranean Domain
<<external>> Surface Domain
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
<<external>> Surface Domain
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
16 intervals of w idth 100 1 - Johnson SB
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
25 intervals of w idth 75 1 - Johnson SB
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
27 intervals of w idth 50 1 - Johnson SB
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
23 intervals of w idth 500 1 - Beta
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
30 intervals of w idth 50 1 - Johnson SB
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
17 intervals of w idth 10 1 - Beta
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.