modeling infrastructure bridges maintenance work zones · international journal of architecture,...

International Journal of Architecture, Engineering and ConstructionVol 5, No 1, March 2016, 21-28

Modeling Infrastructure Bridges Maintenance Work Zones

Mohamed Marzouk∗ and Kouzal El Banna

Structural Engineering Department, Faculty of Engineering, Cairo University, Egypt

Abstract: Costs of infrastructure bridges maintenance are attributed to the cost of maintenance and disruptivedelays to users. To achieve effectiveness in managing the assets of infrastructure systems, many interdisciplinarytools and concepts are integrated and deployed to achieve lifecycle cost optimization in an effort to achievesustainable infrastructures. As bridges become older and maintenance costs become higher, governmentalauthorities responsible for bridges maintenance face challenges with respect to implementation of optimal bridgemanagement programs based on life-cycle cost considerations. This paper presents a model that determinesthe costs associated with each item of bridges maintenance. The model takes into consideration work zone usercosts. It also compares between data in deterministic condition and probabilistic condition using simulationoptimization. A comprehensive case study of El-Giza bridge maintenance is presented to demonstrate thepractical features of the proposed model.

Keywords: Infrastructure bridges, life cycle cost analysis, maintenance costs, optimization

DOI: 10.7492/IJAEC.2016.003

1 INTRODUCTION

Bridges represent a substantial investment of publicfunds, and are expected to provide satisfactory perfor-mance and remain in service for many years. For newbridges, design specifications typically require 75- or100- year design life. Bridges deteriorate over time dueto several factors including weather (Zhu et al. 2007;Mondal and DeWolf 2007), traffic volume, poor designwork, poor quality of construction (Belli et al. 2008).Table (1) lists the factors that influence bridges dete-rioration as reported by Huang et al. (2010) based onliterature (Jiang 1990; Scherer and Glagola 1994; Zhaoand Chen 2002; Su 2003). Moreover, even bridges notsuffering from any serious deterioration may becomeobsolete with time because of increases in legal loadstandards and modifications of bridge design codes.Consequently, as the age of existing bridges increases,more resources need to be allocated for their mainte-nance, rehabilitation, and replacement (ARMY TM 5-600/AFJPAM 32-1088 1994). Several research effortshave been made to diagnose bridges’ deterioration us-ing Markov-chain (Scherer and Glagola 1994), fuzzysystem (Zhao and Chen 2002), logistic regression anal-ysis (Su 2003). It is worth noting that before conduct-ing any action towards existing bridges deteriorationscareful analyses such as an understanding of the symp-

toms and the causative problems are essential in thecondition assessment of bridge structures. This can bedone by site investigations and laboratory tests. Sub-sequently, life cycle cost analysis is carried out in orderto select the most efficient solution for treatment of thebridge.Planning for asset management should take into con-

sideration the overall life cycle costs of providing theservice and be prepared to make investment decisionsaccordingly. Asset management involves several as-pects (InfraGuide 2005), including asset value, life cy-cle management, long-term affordability, risk manage-ment and assessment, performance measurement, op-erational plans, and integration of technical and finan-cial plans. The framework for an asset managementplan can be described in terms of seven questions (In-fraGuide 2005):

1. What do you have and where is it? (Inventory)2. What is it worth? (Costs/replacement rates)3. What is its condition and expected remaining ser-

vice life? (Condition and capability analysis)4. What is the level of service expectation, and what

needs to be done? (Capital and operating plans)5. When do you need to do it? (Capital and oper-

ating plans)6. How much will it cost and what is the acceptable

*Corresponding author. Email: [email protected]

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mailto:[email protected]

Marzouk and Banna/International Journal of Architecture, Engineering and Construction 5 (2016) 21-28

Table 1. Bridge deterioration factorsCriteria Factors

General Factors Bridge age, No. of spans, No. of lanes, Length of bridge, Area/width of deck, Max. span, Skew angleStructural factors Structural type, Girder type, Girder material, Abutment type, Pavement, Earthquake bracing,

Expansion joint, Wing wall, Designed live loadTraffic factors Traffic volume

Environmental factors Over water or not , Distance from coast, Acid rain, Average yearly rainfall, Average rainy daysper year, Soil profile

Others Road level, Climate region

level of risk(s)? (Short- and long-term financialplan)

7. How do you ensure long-term affordability?(Short- and long-term financial plan)

It is recommended that inspections be made annual-ly of all basic structures and more frequently for fend-ers and utilities. Additional inspections may be neces-sary under certain circumstances, such as a tsunami,earthquakes, and accidents. Bridges can be inspect-ed following one of the following types (ARMY TM5-600/AFJPAM 32-1088 1994):

1. Operator inspection: it consists of examination,lubrication, and minor adjustment performed byoperators on a continuous basis.

2. Preventative maintenance inspection: is thescheduled examination and minor repair of fa-cilities and systems that would otherwise not besubject to inspection (e.g., pier fender systems).

3. Control inspection: is the major scheduled exam-ination of all components and systems on a pe-riodic basis to determine and document the con-dition of the bridge and to generate major workrequired.

Rehabilitation of bridges impacts their users in differ-ent aspects; inconvenience to local business and com-munity, noise and environmental impacts (Mallela andSadavisam 2011). Work zone road user costs are usedas economic basis for quantifying these adverse impactswhich can then be used for effective decision-makingto improve work zone mobility and safety (Mallela andSadavisam 2011; Benekohal et al. 2010). This paperpresents a model that utilizes simulation optimizationto analyze life cycle costs of bridges. The model adop-ts metaheuristic optimization as an iterative generationprocess to explore and exploit the search space in aneffort to reach near optimum solutions. Metaheuristicoptimization combine basic heuristic methods in high-er level frameworks aimed at efficiently and effectivelyexploring a search space (Blum and Roli 2003). Themodel has several features: i) it determines the costsassociated to each item of bridge maintenance, ii) itcalculates bridge maintenance costs over the servicelife of the bridge by determining the NPV for thesecosts, iii) it considers work zone user costs, and iv) itcompares deterministic condition data against proba-bilistic condition data using simulation optimization.A numerical example is presented to demonstrate thepractical features of the proposed model.

Work zones often cause traffic congestion on high vol-ume roads. As traffic volumes increase so does workzone-related traffic congestion and so does the pub-lic demand for road agencies to decrease both theirnumber and duration. Negative impacts on road userscan be minimized by bundling interventions on sev-eral interconnected road sections instead of treatingeach road section separately. Negative impacts on roadusers can be quantified in user costs. The optimumwork zone is the one that results in the minimum over-all agency and user costs. The minimization of thesecosts is often the goal of corridor planning. In order toachieve this goal the interventions on each asset type(pavement, bridges, tunnels, hardware, etc.) must bebundled into optimum packages. Hajdin and Linden-mann (2007) presented a method that enables roadagencies to determine optimum work zones and inter-vention packages. The method allows the considerationof both budget constraints and distance constraints,including maximum permissible work zone length orminimum distance between work zones. The mathe-matical formulation of this optimization problem is abinary program that can be solved by existing tech-niques (i.e., the branch-and-bound method).Pavements on two-lane two-way highways are usu-

ally resurfaced by closing one lane at a time. Vehi-cles then travel in the remaining lane along the workzone, alternating directions within each control cycle.Several alternatives can be evaluated, defined by thenumber of closed lanes and fractions of traffic divert-ed to alternate routes. Chen et al. (2005) presentedan algorithm, referred to as SAUASD (Simulated An-nealing for Uniform Alternatives with a Single Detour),to find the best single alternative within a resurfacingproject. SAMASD is developed to search through pos-sible mixed alternatives and their diverted fractions,to minimize total cost, further including agency cost(resurfacing cost and idling cost) and user cost (user de-lay cost and accident cost). Thus, traffic managementplans are developed with uniform or mixed alternativeswithin a two-lane highway resurfacing project. Severalresearch efforts have been made in highway mainte-nance and lane closures (Wang et al. 2002; Lee 2009;Meng and Weng 2010; Yang et al. 2009; Jiang et al.2009; Christodoulou et al. 2012). This paper presentsa framework that is dedicated for determining the op-timum length of highway resurfacing work zone withminimum cost. A numerical example is worked outto demonstrate the essential features of the proposedframework.

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2 LIFE CYCLE COST ANALYSIS

The life cycle cost (LCC) of an asset is defined as thetotal cost, in present value or annual value that in-cludes the initial costs, maintenance, repair and re-newal (MR&R) costs over the service life or a specifiedlife cycle, whereas, life cycle cost analysis (LCCA) is aprocess for evaluating the total economic worth of a us-able project investment by analyzing initial costs anddiscounted future costs, such as maintenance, use, re-construction, rehabilitation, restoring, resurfacing, anddisposal costs, over the life of the project segment(Rahman and Vanier 2004). LCCA is to estimate theoverall costs of treatment methods or options and se-lect the best one that ensures the facility will providethe lowest overall cost of ownership consistent with itsquality and function (Humphreys et al. 2007). A prob-abilistic life cycle costing analysis can be used to obtaina more realistic assessment of the benefits of innovativematerials and technologies, whilst giving asset manag-er a basis to arrive at an acceptable level of risk, takinginto account the reliability of proven/traditional solu-tions weighed against innovative solutions (Humphreyset al. 2007). One technique that has been used to ac-count for the inherent uncertainty that is being wide-ly promoted for incorporation in the evaluation of in-frastructure projects specifically in (LCCA) is MonteCarlo Simulation. This technique randomly samplesvalues for the uncertain input parameters accordingto their pre-constructed probability distributions andrecords the responses from the model, in the case ofthe (LCCA) model, for the sampled values. This pro-cess is iterated numerous times until the preset conver-gence criteria are met, after which the recorded systemresponses are used to construct the probability distri-bution of the outcome, the NPV as Equation (1).

NPV =

n∑t=0

Ct

(1 + i)t(1)

Where; NPV = Net Present Value of life cycle costs,Ct = sum of all relevant costs occurring in year t, n =length of analyzed period, and i = discount rate.

3 WORK ZONE USER COSTS

Work zone user costs are the increased vehicle oper-ating cost, delay, and crash costs to highway usersresulting from construction, maintenance, or rehabil-itation work zones. These costs are function of thetiming, duration, frequency, scope, and characteristicsof the work zone; the volume and operating character-istics of the traffic affected; and the dollar cost ratesassigned to vehicle operating, delay, and crashes. WorkZone is defined as an area of a highway where main-tenance and construction operations impact the num-ber of lanes available to traffic or affect the operationalcharacteristics of traffic flowing through the area (Wall-

s III and Smith 1998). Each work zone is associatedwith a different user costs. As such, each work zoneshould be evaluated separately when characteristics ofthe work zone or the characteristics of the affected traf-fic change. Bridge rehabilitation and maintenance ac-tivities generally occur at different points in the analy-sis period with different traffic, and they generally varyin scope and duration. The time that they occur alsoaffects the influence of the discount factor used in de-veloping NPV (Walls III and Smith 1998). Schonfeldand Chien (1999) developed a work zone cost functionwhich includes user delay cost and maintenance cost asper Equation (2).

CT = CM + CU (2)

Where; CT is total cost per lane-kilometer; CM ismaintenance cost per lane-kilometer; and CU is userdelay cost per lane-kilometer.The user delay cost consists of the queuing delay

costs through work zones. Zone delay cost without anyalternate route around the work zone and is calculatedbased on Equation (3).

Cq =(Z3 + Z4L)[Q1(

3600H −Q1) +Q2(

3600H −Q2)]v

V ( 3600H −Q1 −Q2)

(3)

Where; Cq is queuing delay cost per lane-kilometer;Z3 is setup time; Z4 is average maintenance time perlane-kilometer; L is work zone length; Q1 is hourly flowrate in Direction 1; Q2 is hourly flow rate in Direction2; H is average headway; V is average work zone speed;v is value of user time; and Z3 + Z4L represents thetotal maintenance duration per zone. Equation 3 rep-resents the queuing delay cost due to one-way trafficcontrol, the moving delay cost of the traffic flow Q1

and Q2, denoted as Cv is the cost increment due tothe work zone. It is calculated based on Equation (4)after considering the following factors (Marzouk et al.2011):

1. The average maintenance duration per kilometerZ3

L + Z4

2. The travel time difference over zone length withthe work, L

V , and without the work zone, LV0, and

3. The value of time, v, thus:

Cv = (Q1 +Q2)(Z3

L+ Z4)(

L

V− L

V0)v (4)

Where; V0 represents the speed on the original roadwithout any work zone. The user delay cost for thissolution CU is equal to the sum of queue delay cost Cq

and moving delay cost Cv as per Equation (5):

CU = Cq + Cv (5)

The accident cost incurred by the traffic passing thework zone can be determined from the number of ac-cidents per 100 million vehicle hours multiplied by theproduct of the increasing delay (

Cq

v + Cv

v ) and the aver-

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age cost per accident va. As such, the average accidentcost per lane-kilometer Ca is formulated as per Equa-tion (6) (Fouad 2011):

Ca =[(Z3+Z4L)[Q1(

3600H −Q1)+Q2(

3600H −Q2)]v

V ( 3600H −Q1−Q2)

]nava

108(6)

The maintenance cost per zone is assumed to beZ1 + Z2L, where Z1 is fixed setup cost; and Z2 is av-erage maintenance cost per additional lane-kilometer.The average maintenance cost per lane-kilometer, CM ,is the total maintenance cost per zone divided by thezone length L as per Equation (7). Then, the total costfor this solution as Equation (8):

CM =Z1 + Z2L

L=

Z1

L+ Z2 (7)

CT = CM + CU + Ca (8)

The developed simulation module captures the se-quence of tasks involved in the resurfacing operationand the relationships between these tasks. The proce-dure of designing and building a simulation model canbe summarized as following:

1. Break-down the operation into main processesand tasks. For each task, type of resources (i.e.,materials, labor, and/or equipment) involved inits execution is identified.

2. Indicated each type of tasks, either: Normal orCombi depending on its need of resources.

3. Representing the sequence and relationships be-tween tasks by using Arcs to map the network.

4. Add more control logical conditions by createdcontrol statements, which cannot be modeled us-ing normal arcs and tasks.

5. Using simulation language to code the simulationnetwork and control statements.

6. Verify the simulation model and test it.

4 OPTIMIZING WORK ZONE USERSCOSTS

The objective of the work zone optimization problemis to minimize the total cost for work zone activities.The objective function for work zone activities can beexpressed as per Equation (9):

Min CT = CM + CU + Ca (9)

Where; CT is total cost, CM is maintenance cost, andCU is user cost.The controllable variable affecting CM include work

zone length, fixed setup cost, and average maintenancecost per unit length; the controllable variables affect-ing CU include work zone length, traffic volume, speed,etc. Both CM and CU are function of work zone length.It should be noted that longer zones tend to increasethe users delays, but the maintenance activities can beperformed more efficiently with fewer repeated setups

in longer zones. Since work zones lengths and main-tenance duration affect maintenance and user cost, itis important to determine the tradeoff between main-tenance cost and user cost in order to minimize totalcost (Marzouk and Fouad 2014; Fouad 2011).Maintenance cost usually includes labor cost, equip-

ment cost, material cost and traffic management cost.The first step in estimating maintenance cost is to de-termine construction quantities/unit prices. In thisresearch, the cost of maintaining cost of length L isassumed to be a linear function, of the form CM =Z1+Z2L, in which Z1 represents the fixed cost for set-ting up a work zone and Z2 is the average additionalmaintenance cost per work zone unit length. The com-ponents of user cost user delay cost and accident cost.The user delay can be classified into queuing delay andmoving delay. The user delay cost is determined bymultiplying the user delay by the value of user time(Marzouk et al. 2011).The accident cost is related tothe historical accident rate, delay, work zone configura-tion, and average cost per accident. Optimization vari-ables are any entities within studied system, where anychange in this entity would seriously affect the observedoptimization functions. Based on interviews with ex-pert engineers and extensive analysis of resurfacing op-eration, optimization variables have been determined.The considered optimization variables are:

1. Hourly Flow Rate in Direction 1 (Q1): Numberof vehicle in the same direction with work zone.

2. Hourly Flow Rate in Direction 2 (Q2): Numberof vehicle in opposite direction against work zone.

3. Average maintenance time per lane-kilometer(Z4): the required duration for maintenance foreach lane per kilometer.

4. Work zone length (L): the optimum length forwork zone that decreases delay in traffic time anddecrease accidents.

5. Average work zone speed (V ): speed of vehicle atwork zone.

6. Average headway (H): the time of the distancebetween two vehicles.

5 NUMERICAL EXAMPLE

In order to demonstrate the use of the proposed simula-tion optimization model in optimizing bridges rehabil-itation, an actual project example is considered of El-Giza Bridge. The Bridge is considered the most impor-tant bridges in El-Giza Governorate-Egypt. The bridgeconnects El-Harm, Faisl, and Munib streets to CairoUniversity, Murad, and Abbas streets (see Figure 1).The example considers maintenance of 1 Km length.Table 2 lists input values for the different consideredparameters. These values are either taken based onliterature (Fouad 2011) (e.g., cost of user time value,number of accidents per 100 million vehicle hours, av-erage cost per accident, interest factor), feedback from

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Table 2. Example input parameters

Parameters Valuev: cost of user time value 12.7 LE/Veh.hrV0: road speed at normal condition (without any work zone) 80 Km/hrna: number of accidents per 100 million vehicle hours 67 accident/100mvhva: average cost per accident 17.6 LE/hrr: interest factor 8%I: inflation Index 5%Z1: fixed cost for setting up a work zone 700,000 LEZ3: standing time 10 hr/zone

Table 3. Bridge maintenance cost items

Cost Items Unit Quantity LE/Unit Z2 (LE/Km)Minimum Most likely Maximum

Expansion joints Lm 30 5,800 165,300 174,000 182,700Fence works Lm 250 750 178,125 187,500 196,875Brushes bridges metal flooring m2 2500 485 1,151,875 1,212,500 1,273,125Paint metal sectors in bridge m2 40000 78 2,964,000 3,120,000 3,276,000Maintaining bridge shoulders Lm 350 350 116,375 122,500 128,625Maintaining bridge supports No. 16 1,250 19,000 20,000 21,000

Z2 (LE/Km) Total 4,594,675 4,836,500 5,078,325

Table 4. Optimization parameters

Parameter RangeQ1 3000-5000 Veh/hrQ2 0 Veh/hr (one way bridge)Z4 10-20 Hr/Lane.Km

L (work zone length) 0.1-1 KmV 10-15 Km/hrH 2-10 Sec

Table 5. Estimated net present value over bridge life

Year Inflation rate Interest rate NPV (LE)10 1.63 0.4632 4,572,99220 2.65 0.2145 3,450,29230 4.32 0.0994 2,603,22340 7.04 0.046 1,964,11450 11.47 0.0213 1,481,91160 18.68 0.0099 1,118,09270 30.43 0.0046 843,593

NPV Total 16,034,218

*Note: For year n, Inflation rate = (1 + I)n, Interest rate = 1(1+r)n

NPV = 6,061,010 ∗ Inflation rate ∗ Interest rate

construction practitioners (e.g., interest factor, infla-tion index, fixed cost for setting up a work zone, stand-ing time), or actual data of the bridge (e.g., road speedat normal condition). Table 3 lists Maintenance Cost(CM ) and the average additional maintenance cost perwork zone unit length (Z2) for the different items ofbridge maintenance. Triangle distribution has been as-sumed for the average additional maintenance cost perwork zone unit length (Z2). Optimization parametersare listed in Table 4. Applying the input parameters inEquation (4), the moving delay cost (Cv) is estimatedto be 60,008 LE/Lane.Km. Whereas, the queue delaycost (Cq) is estimated to be 71,120 LE/Lane.Km. By

applying in the input parameters in Equation 6, thevalue of the average accident cost (Ca) is very minorand it can be neglected. The bridge consists of fourlanes and it has one closure lane, also one kilometerlength, as such; the total cost is estimated as follows,considering the values of most likely maintenance cost,given in Table 3:

CT = CM + CU + Ca

= 4, 836, 500 + 700, 000 + 4 ∗ (60, 008 + 71, 120)= 6, 061, 010LE.

The net present value (NPV) for the bridge over itslife is estimated using Table 5. Considering 70 yearsand the maintenance takes place every 10 years, the

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Figure 1. Layout of El-Giza bridge

Figure 2. Net present value simulation results

Figure 3. Maintenance optimum solution

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total net present value is 16,034,218 LE. Subsequent-ly, 2000 simulation runs were executed. The NPV netpresent value changes from 16,166,787 to 15,315,092.For certainty level 95.00%, the NPV net present valuechanges from 14,830,198 to 15,536,178 (see Figure 2).The optimum solution is obtained at 15,315,092, con-sidering the values of the parameters, given in Figure 3.

6 SUMMARYThe level of deterioration in bridges depends on manyfactors including corrosion of reinforcing steel, condi-tion of concrete and external environments. One of thecritical issues causing reduced service life of the bridgewas a delay of conducting bridge maintenance. Fur-thermore, delaying bridge maintenance causes increasein cost due to repair and rehabilitation. This paper pre-sented a model that is capable to determine the costsassociated with each item of bridges maintenance. Themodel takes into consideration work zone user costs. Anumerical example, of El-Giza Bridge, was presented todemonstrate the use of the proposed simulation opti-mization model in optimizing bridges rehabilitation.

REFERENCES

ARMY TM 5-600/AFJPAM 32-1088 (1994). BridgeInspection, Maintenance, and Repair. Departmentsof the Army and the Airforce, Virginia, USA.

Belli, K., Wadia-Fascetti, S., and Rappaport, C.(2008). “Model based evaluation of bridge decks us-ing ground penetrating radar.” Computer-Aided Civ-il and Infrastructure Engineering, 23(1), 3–16.

Benekohal, R. F., Ramezani, H., and Avrenli, K. A.(2010). Queue and users’ costs in highway workzones. Illinois Center for Transportation Series No.10-075, IL, USA.

Blum, C. and Roli, A. (2003). “Metaheuristics incombinatorial optimization: overview and conceptu-al comparison.” ACM Computing Surveys (CSUR),35(3), 268–308.

Chen, C. H., Schonfeld, P., and Paracha, J. (2005).“Work zone optimization for two-lane highway resur-facing projects with an alternate route.” Transporta-tion Research Record, (1911), 51–66.

Christodoulou, S., Lukas, K., and Borrmann, A.(2012). “Entropy-based traffic impact analysis andoptimization of roadway maintenance.” Proceedingsof the 14th International Conference on Computingin Civil and Building Engineering, Moscow, Russia.

Fouad, M. (2011). “Optimizing highways resurfac-ing using computer simulation.” M.S. thesis, CairoUniversity, Cairo, Egypt.

Hajdin, R. and Lindenmann, H. P. (2007). “Algorithmfor the planning of optimum highway work zones.”Journal of infrastructure systems, 13(3), 202–214.

Huang, R., Mao, I., Lee, H., et al. (2010). “Explor-

ing the deterioration factors of RC bridge decks: Arough set approach.” Computer-Aided Civil and In-frastructure Engineering, 25(7), 517–529.

Humphreys, M., Setunge, S., Fenwick, J., and Alwi, S.(2007). “Strategies for minimising the whole of lifecycle cost of reinforced concrete bridge exposed toaggressive environments.” Proceedings of the SecondInternational Conference on Quality Chain Manage-ment, Stockholm.

InfraGuide (2005). Managing Infrastructure Assets.Federation of Canadian Municipalities (FCM), Ot-tawa, Ontario, Canada.

Jiang, Y. (1990). “The development of performanceprediction and optimization models for bridge man-agement systems.” Ph.D. thesis, Purdue University,United States.

Jiang, Y., Chen, H., and Li, S. (2009). “Computationof user costs at freeway work zones using weigh-in-motion traffic data.” International Journal of Con-struction Education and Research, 5(3), 197–219.

Lee, H. Y. (2009). “Optimizing schedule for improvingthe traffic impact of work zone on roads.” Automa-tion in Construction, 18(8), 1034–1044.

Mallela, J. and Sadavisam, S. (2011). “Work zone roaduser costs concepts and applications.” Report No.FHWA-HOP-12-005, Federal Highway Administra-tion, Washington, D.C., USA.

Marzouk, M. and Fouad, M. (2014). “Simulation-basedmodel for optimizing highways resurfacing opera-tions.” The Baltic Journal of Road and Bridge Engi-neering, 9(1), 58–65.

Marzouk, M., Fouad, M., and El-Said, M. (2011). “Sim-ulation of resurfacing pavement operation of high-ways under lane closure condition.” Proceedings ofthe 28th International Symposium on Automationand Robotics in Construction, Seoul, Korea.

Meng, Q. and Weng, J. (2010). “Cellular automa-ta model for work zone traffic.” Transportation Re-search Record, 2188, 131–139.

Mondal, P. and DeWolf, J. T. (2007). “Developmentof computer-based system for the temperature mon-itoring of a post-tensioned segmental concrete box-girder bridge.” Computer-Aided Civil and Infrastruc-ture Engineering, 22(1), 65–77.

Rahman, S. and Vanier, D. J. (2004). “Life cycle costanalysis as a decision support tool for managing mu-nicipal infrastructure.” Proceedings of the CIB Tri-ennal Congress, Toronto, Canada.

Scherer, W. T. and Glagola, D. M. (1994). “Markovianmodels for bridge maintenance management.” Jour-nal of Transportation Engineering, 120(1), 37–51.

Schonfeld, P. and Chien, S. (1999). “Optimal work zonelengths for two-lane highways.” Journal of Trans-portation Engineering, 125(1), 21–29.

Su, H. (2003). “A correlation study of the existingbridges for failure analysis-case study of taichungcounty.” M.S. thesis, Feng Chia University, Taichung,Taiwan.

27


Walls III, J. and Smith, M. R. (1998). “Life-cycle costanalysis in pavement design-interim technical bul-letin.” Report No. FHWA-SA-98-079, FHWA, Wash-ington, D.C., USA.

Wang, Y., Cheu, R. L., and Fwa, T. F. (2002). “High-way maintenance scheduling using genetic algorithmwith microscopic traffic simulation.” Proceedings ofthe 81st annual meeting of the Transportation Re-search Board, 13–17.

Yang, N., Schonfeld, P., and Kang, M. W. (2009). “Ahybrid methodology for freeway work-zone optimiza-

tion with time constraints.” Public Works Manage-ment & Policy, 13(3), 253–264.

Zhao, Z. and Chen, C. (2002). “A fuzzy system for con-crete bridge damage diagnosis.” Computers & struc-tures, 80(7), 629–641.

Zhu, Z., Gu, M., and Chen, Z. (2007). “Wind tun-nel and cfd study on identification of flutter deriva-tives of a long-span self-anchored suspension bridge.”Computer-Aided Civil and Infrastructure Engineer-ing, 22(8), 541–554.

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