Bridge network maintenance prioritization under budget constraint

Abstract This study develops a decision model to assist bridge authorities in determining a preferred maintenance prioritization schedule for a degraded bridge network in a community that optimizes the performance of transportation systems within budgetary constraints at a regional scale. The study utilizes network analysis methods, structural reliability principles and meta-heuristic optimization algorithms to integrate individual descriptive parameters such as bridge capacity rating, condition rating, traffic demand, and location of the bridge, into global objective functions that define the overall network performance and maintenance cost. The performance of the network is measured in terms of travel time between all possible origin-destination (O-D) pairs. In addition to the global budgetary constraint, the optimization is also conditioned on local constraints imposed on traffic flow by insufficient load carrying capacity of deficient bridges. Uncertainties in traffic demands, vehicle weights and maintenance costs are also considered in the problem formulation. Two project priority indices are introduced – the static priority index (SPI), defined as a function of the difference in network travel time between block running (with reduced load carrying capacity before repair) and smooth running (design-level load carrying capacity after repair) of a bridge, and the dynamic priority index (DPI) defined as the likelihood of a bridge being selected for repair when the budget is fixed and the uncertainties governing the performance of the transportation network are considered. Finally, this decision model is illustrated with a hypothetical network with 160 bridges.

[1]  André D. Orcesi,et al.  A bridge network maintenance framework for Pareto optimization of stakeholders/users costs , 2010, Reliab. Eng. Syst. Saf..

[2]  Donald E. Grierson,et al.  Comparison among five evolutionary-based optimization algorithms , 2005, Adv. Eng. Informatics.

[3]  Dan M. Frangopol,et al.  Connectivity-Based Optimal Scheduling for Maintenance of Bridge Networks , 2013 .

[4]  D. K. Pratihar,et al.  Particle Swarm Optimization Algorithm vs Genetic Algorithm to Develop Integrated Scheme for Obtaining Optimal Mechanical Structure and Adaptive Controller of a Robot , 2011 .

[5]  O. Weck,et al.  A COMPARISON OF PARTICLE SWARM OPTIMIZATION AND THE GENETIC ALGORITHM , 2005 .

[6]  Hong Kam Lo,et al.  A capacity related reliability for transportation networks , 1999 .

[7]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[8]  Samer Madanat,et al.  Determination of Optimal MR&R Policies for Retaining Life-Cycle Connectivity of Bridge Networks , 2015 .

[9]  Fred W. Glover,et al.  One-pass heuristics for large-scale unconstrained binary quadratic problems , 2002, Eur. J. Oper. Res..

[10]  Endre Boros,et al.  A max-flow approach to improved lower bounds for quadratic unconstrained binary optimization (QUBO) , 2008, Discret. Optim..

[11]  Dan M. Frangopol,et al.  Lifetime-oriented multi-objective optimization of structural maintenance considering system reliability, redundancy and life-cycle cost using GA , 2009 .

[12]  Nobuoto Nojima Performance-Based Prioritization for Upgrading Seismic Reliability of a Transportation Network , 1998 .

[13]  Dan M. Frangopol,et al.  Life-cycle performance, management, and optimisation of structural systems under uncertainty: accomplishments and challenges 1 , 2011, Structures and Infrastructure Systems.

[14]  Bernd Freisleben,et al.  Greedy and Local Search Heuristics for Unconstrained Binary Quadratic Programming , 2002, J. Heuristics.

[15]  Narayana Prasad Padhy,et al.  Comparison of Particle Swarm Optimization and Genetic Algorithm for TCSC-based Controller Design , 2007 .

[16]  Stephanie E. Chang,et al.  Life-Cycle Cost Analysis with Natural Hazard Risk , 1996 .

[17]  J. Scott Provan,et al.  The Complexity of Counting Cuts and of Computing the Probability that a Graph is Connected , 1983, SIAM J. Comput..

[18]  Ravindra K. Ahuja,et al.  Network Flows: Theory, Algorithms, and Applications , 1993 .

[19]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[20]  Bruce R. Ellingwood,et al.  MAINTAINING RELIABILITY OF CONCRETE STRUCTURES. II: OPTIMUM INSPECTION/REPAIR , 1994 .

[21]  Niladri Chakraborty,et al.  Comparative Performance Study of Genetic Algorithm and Particle Swarm Optimization Applied on Off-grid Renewable Hybrid Energy System , 2011, SEMCCO.

[22]  Dan M. Frangopol,et al.  Time-Dependent Bridge Network Reliability: Novel Approach , 2005 .

[23]  Gintaras Palubeckis A Tight Lower Bound for a Special Case of Quadratic 0–1 Programming , 2005, Computing.

[24]  Dan M. Frangopol,et al.  Balancing Connectivity of Deteriorating Bridge Networks and Long-Term Maintenance Cost through Optimization , 2005 .

[25]  Kengo Katayama,et al.  Performance of simulated annealing-based heuristic for the unconstrained binary quadratic programming problem , 2001, Eur. J. Oper. Res..

[26]  Dan M. Frangopol,et al.  Optimizing Bridge Network Maintenance Management under Uncertainty with Conflicting Criteria: Life-Cycle Maintenance, Failure, and User Costs , 2006 .

[27]  Hideomi Ohtsubo,et al.  Reliability-Based Structural Optimization , 1991 .

[28]  David De Leon,et al.  Determination of optimal target reliabilities for design and upgrading of structures , 1997 .

[29]  Panos M. Pardalos,et al.  Lower Bound Improvement and Forcing Rule for Quadratic Binary Programming , 2006, Comput. Optim. Appl..

[30]  Y. K. Wen,et al.  Minimum Building Life-Cycle Cost Design Criteria. I: Methodology , 2001 .

[31]  Dan M. Frangopol,et al.  Repair Optimization of Highway Bridges Using System Reliability Approach , 1999 .

[32]  Hong Kam Lo,et al.  Capacity reliability of a road network: an assessment methodology and numerical results , 2002 .

[33]  Y Iida,et al.  AN APPROXIMATION METHOD OF TERMINAL RELIABILITY OF ROAD NETWORK USING PARTIAL MINIMAL PATH AND CUT SETS , 1989 .

[34]  Alan Nicholson,et al.  DEGRADABLE TRANSPORTATION SYSTEMS: SENSITIVITY AND RELIABILITY ANALYSIS , 1997 .

[35]  Yasuo Asakura,et al.  Road network reliability caused by daily fluctuation of traffic flow , 1991 .

[36]  Chien-Ching Chiu,et al.  Comparison of Particle Swarm Optimization and Genetic Algorithm for the Path Loss Reduction in an Urban Area , 2012 .

[37]  Dan M. Frangopol,et al.  Probability-Based Bridge Network Performance Evaluation , 2006 .

[38]  Brunilde Sansò,et al.  Performability of a Congested Urban Transportation Network When Accident Information is Available , 1999, Transp. Sci..

[39]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[40]  Ross B. Corotis,et al.  Multi-attribute aspects for risk assessment of natural hazards , 2010 .