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Multiobjective Optimization for Risk-Based Maintenance and Life-Cycle Cost of Civil Infrastructure Systems

Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 199)

Abstract

Reliability and durability of civil infrastructure systems such as highway bridges play a very important role in sustainable economic growth and social development of any country. The bridge infrastructure has been undergoing severe safety and condition deterioration due to gradual aging, aggressive environmental stressors, and increasing traffic loads. Maintenance needs for deteriorating highway bridges, however, have far outpaced available scarce funds highway agencies can provide. Bridge management systems (BMSs) are thus critical to cost-effectively allocate limited maintenance resources to bridges for achieving satisfactory lifetime safety and performance. In existing BMSs, however, visual inspections are the most widely adopted practice to quantify and assess bridge conditions, which are unable to faithfully reflect structural capacity deterioration. Failure to detect structural deficiency due to, for example, corrosion and fatigue, and inability to accurately assess real bridge health states may lead to unreliable bridge management decisions and even enormous safety and economic consequences. In this paper, recent advances in risk-based life-cycle maintenance management of deteriorating civil infrastructure systems with emphasis on high-way bridges are reviewed. Methods of predicting lifetime safety and performance of highway bridges with and without maintenance are discussed. Treatment of various uncertainties associated with the complex deterioration processes due to time-dependent loading, environmental stressors, structural resistances, and maintenance actions are emphasized. The bridge maintenance management is formulated as a nonlinear, discrete, combinatorial optimization problem with simultaneous consideration of multiple and conflicting objectives, which address bridge safety and performance as well as long-term economic consequences. The effectiveness of genetic algorithms as a numerical multiobjective optimizer for producing Pareto-optimal tradeoff solutions is demonstrated. The proposed probabilistic multiobjective optimization BMS is applied at project-level for similar bridges and at network-level for a group of different bridges that form a highway network.

Keywords

System reliability optimization civil infrastructure bridges genetic algorithms 

References

  1. [1]
    AASHTO. Manual for Condition Evaluation of Bridges. 2nd. edn. American Association of State Highway and Transportation Officials, Washington, D.C., 1994.Google Scholar
  2. [2]
    AASHTO. Standard Specifications for Highway Bridges. 16th edn. American Association Of State Highway And Transportation Officials, Washington, D.C., 1996.Google Scholar
  3. [3]
    F. Akgül, D. M. Frangopol. Rating and Reliability of Existing Bridges in a Network. Journal of Bridge Engineering, ASCE 8(6), 383–393, 2003.CrossRefGoogle Scholar
  4. [4]
    M. G. H. Bell, Y. Iida. Transportation Network Analysis. Wiley, Chichester, 1997.Google Scholar
  5. [5]
    A. Chen, W. W. Recker. Considering Risk Taking Behavior in Travel Time Reliability, Report No. UCI-ITS-WP-00-24, Institute of Transportation Studies, University of California, Irvine, California, 2000.Google Scholar
  6. [6]
    C. A. Coello Coello Theoretical and Numerical Constraint-Handling Techniques Used with Evolutionary Algorithms: a Survey of the State of the Art.” Computer Methods in Applied Mechanics and Engineering 191, 1245–1287, 2002.MATHMathSciNetCrossRefGoogle Scholar
  7. [7]
    C.A. Coello Coello, D.A. van Veldhuizen, G.B. Lamont. Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, New York, 2002.Google Scholar
  8. [8]
    P.C. Das. Prioritization of Bridge Maintenance Needs. In: Frangopol, D.M. (ed.): Case Studies in Optimal Design and Maintenance Planning Of Civil Infrastructure Systems. ASCE Reston, 26–44, 1999.Google Scholar
  9. [9]
    DB12/01. The Assessment of Highway Bridge Structures. Highways Agency Standard for Bridge Assessment, London, 2001.Google Scholar
  10. [10]
    K. Deb. Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester, 2001.Google Scholar
  11. [11]
    S. Denton. Data Estimates for Different Maintenance Options for Reinforced Concrete Cross Heads (Personal communication). Parsons Brinckerhoff Ltd, Bristol, 2002.Google Scholar
  12. [12]
    Enright, M. P., Frangopol, D. M. Condition Prediction of Deteriorating Concrete Bridges Using Bayesian Updating. Journal of Structural Engineering, ASCE 125(10) (1999) 1118–1125.Google Scholar
  13. [13]
    A.C. Estes, D.M. Frangopol. Updating Bridge Reliability Based on Bridge Management Systems Visual Inspection Results. Journal of Bridge Engineering, ASCE 8(6), 374–382, 2004.CrossRefGoogle Scholar
  14. [14]
    FHWA. The Status of The Nation’s Highways, Bridges, and Transit: Conditions and Performance. U.S. Federal Highway Administration, Washington, D.C., 2002.Google Scholar
  15. [15]
    FHWA. Recommendations for Bridge and Tunnel Security. U.S. Federal Highway Administration, Washington, D.C., 2003.Google Scholar
  16. [16]
    D. M. Frangopol, J. S. Kong, E. S. Gharaibeh. Reliability-Based Life-Cycle Management of Highway Bridges. Journal of Computing In Civil Engineering, ASCE 15(1), 27–34, 2001.CrossRefGoogle Scholar
  17. [17]
    H. Furuta, T. Kameda, Y. Fukuda, D.M. Frangopol. Life-Cycle Cost Analysis for Infrastructure Systems: Life Cycle Cost vs. Safety Level vs. Service Life. In: Frangopol, D.M., Brühwiler, E., Faber, M.H., Adey B. (eds.): Life-Cycle Performance of Deteriorating Structures: Assessment, Design and Management. ASCE Reston, Virginia, 19–25, 2004.Google Scholar
  18. [18]
    F. Glover, M. Laguna. Tabu Search. Kluwer Academic Publishers, New York, 1997.Google Scholar
  19. [19]
    D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, 1989.Google Scholar
  20. [20]
    H. Hawk, E. P. Small. The BRIDGIT Bridge Management System. Structural Engineering International, IABSE 8(4), 309–314, 1998.Google Scholar
  21. [21]
    C. Liu, A. Hammad, Y. Itoh,: Multiobjective Optimization of Bridge Deck Rehabilitation Using a Genetic Algorithm. Computer-Aided Civil and Infrastructure Engineering, 12(1) 431–443, 1997.CrossRefGoogle Scholar
  22. [22]
    M. Liu, D. M. Frangopol. Bridge Annual Maintenance Prioritization under Uncertainty by Multiobjective Combinatorial Optimization. Computer-Aided Civil and Infrastructure Engineering, 20(5), 343–353, 2005.CrossRefGoogle Scholar
  23. [23]
    M. Liu, D. M. Frangopol. Balancing the Connectivity Reliability of Deteriorating Bridge Networks and Long-Term Maintenance Cost Using Optimization. Journal of Bridge Engineering, ASCE 10(4), 468–481, 2005.CrossRefGoogle Scholar
  24. [24]
    Z. Lounis, D. J. Vanier. A Multiobjective and Stochastic System for Building Maintenance Management. Computer-Aided Civil and Infrastructure Engineering. 15 (2000) 320–329.CrossRefGoogle Scholar
  25. [25]
    R. Marett, M. Wright. A Comparison of Neighborhood Search Techniques for Multi-objective Combinatorial Problems. Computers & Operations Research. 23(5), 465–483, 1996.CrossRefGoogle Scholar
  26. [26]
    A. Miyamoto, K. Kawamura, H. Nakamura. Bridge Management System and Maintenance Optimization for Existing Bridges. Computer-Aided Civil and Infrastructure Engineering. 15, 45–55, 2000.CrossRefGoogle Scholar
  27. [27]
    M. E. Moore, B. M. Phares, B. A. Graybeal, D. D. Rolander, G. A. Washer. Reliability of Visual Inspection for Highway Bridges (Report Nos. FHWA-RD-01-020 and FHWA-RD-01-21). U.S. Federal Highway Administration, Washington, D.C., 2001.Google Scholar
  28. [28]
    NCHRP. Bridge Life-Cycle Cost Analysis (Report 483). National Cooperative Highway Research Program, Transportation Research Board, Washington, D.C., 2003.Google Scholar
  29. [29]
    R. W. Shepard, M. B. Johnson, W. E. Robert, A. R. Marshall. Modeling Bridge Network Performance-Enhancing Minimal Cost Policies. In: Watanabe, E., Frangopol, D.M., Utsunomiya, T. (eds.): Bridge Maintenance, Safety, Management and Cost. A. A. Balkema Publishers, Leiden, Book and CD-ROM, 2004.Google Scholar
  30. [30]
    P.D. Thompson, E.P. Small, M. Johnson, A.R. Marshall. The Pontis Bridge Management System. Structural Engineering International, IABSE. 8(4), 303–308, 1998.Google Scholar

Copyright information

© International Federation for Information Processing 2006

Authors and Affiliations

  1. 1.Department of Civil, Environmental, and Architectural EngineeringUniversity of ColoradoBoulderUSA
  2. 2.Department of Department of Civil, Environmental, and Architectural EngineeringUniversity of ColoradoBoulderUSA

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