Preference-based maintenance planning for deteriorating bridges under multi-objective optimisation framework

Structure and Infrastructure Engineering

Volumn 7, Issue 7-8, 2011, Pages 633-644

  Lee, Sun Young ^a   Park, Wonsuk ^b   Ok, Seung Yong ^c   Koh, Hyun Moo ^c  

^a Konkuk University   (South Korea)

^b Building no 35 506   (South Korea)

^c Seoul National University   (South Korea)

Author keywords

Bridge maintenance planning; Genetic algorithm; Multi objective optimisation; Preference based decision making

Indexed keywords

BRIDGE MAINTENANCE; BRIDGE PERFORMANCE; DECISION MAKING PROCESS; DESIGN OBJECTIVES; GIRDER BRIDGES; LIFE SPAN; MAINTENANCE ACTION; MAINTENANCE COST; MAINTENANCE PLANNING; MAINTENANCE SCENARIO; MAINTENANCE STRATEGIES; MULTIOBJECTIVE OPTIMISATION; NUMERICAL RESULTS; OPTIMAL MAINTENANCE; OPTIMAL SCHEDULE; OPTIMAL SOLUTIONS; PERFORMANCE PROFILE; PREFERENCE-BASED; PREFERENCE-BASED DECISION MAKING; SEARCHING TECHNIQUES; STATISTICAL REGRESSION; TIME-DEPENDENT;

BRIDGE COMPONENTS; COSTS; DECISION MAKING; DETERIORATION; GENETIC ALGORITHMS; MULTIOBJECTIVE OPTIMIZATION; NUMERICAL METHODS; OPTIMAL SYSTEMS; PLANNING;

MAINTAINABILITY;

EID: 79952691166     PISSN: 15732479     EISSN: 17448980     Source Type: Journal    
DOI: 10.1080/15732479.2010.501565     Document Type: Article

References (28)

1. Coello, C.A.C. and Pulido, G.T., 2001. A micro-genetic algorithm for multi-objective optimisation. In: Proceedings of the 1st international conference on evolutionary multi-criterion optimisation (Lecture Notes in Computer Science, No. 1993), 7-9 March, Zurich, Switzerland. Berlin: Springer, 126-140.
2. Corne, D.W., Knowles, J.D., and Oates, M.J., 2000. The Pareto envelope-based selection algorithm for multiobjective optimisation. In: Proceedings of the parallel problem solving from nature VI conference (Lecture Notes in Computer Science, No. 1917), 18-20 September, Paris, France. Berlin: Springer, 839-848.
3. Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T., 2002. A fast and elitist multi-objective genetic algorithm: NSGA-II. Evolutionary Computation, 6, 182-197.
4. Deb, K., Sundar, J., Uday, N., and Chaudhuri, S., 2006. Reference point based multi-objective optimisation using evolutionary algorithms. International Journal of Computational Intelligence Research, 2, 273-286.
5. Deb, K. and Kumar, A., 2007. Interactive evolutionary multi-objective optimisation and decision-making using reference direction method. In: Proceedings of 9th annual conference on the genetic and evolutionary computation (GECCO-2007), 7-11 July, London, England. New York: ACM, 781-788.
6. Erickson, M., Mayer, A., and Horn, J., 2001. The niched Pareto genetic algorithm 2 applied to the design of ground water remediation systems. In: Proceedings of the 1st international conference on evolutionary multi-criterion optimisation, 7-9 March, Zurich, Switzerland. Berlin: Springer, 681-695.
7. Fonseca, C.M. and Fleming, P.J., 1995. An overview of evolutionary algorithms in multi-objective optimisation. Evolutionary Computation, 3 (1), 1-16.
8. Frangopol, D.M., Lin, K.-Y., and Estes, A.C., 1997. Lifecycle cost design of deteriorating structures. Journal of Structural Engineering, 123 (10), 1390-1401.
9. Frangopol, D.M., Enright, M.P., Gharaibeh, E., and Estes, A.C., 1999. Towards optimal reliability-based bridge maintenance planning. Case studies in optimal design and maintenance planning of civil infrastructures system. Reston, VA: ASCE, 73-88.
10. Furuta, H., Kawamura, Y., Arimura, H., Kojima, T., Kawaoka, Y., Tanaka, M., and Kaneyoshi, M., 1999. Application of GA to erection control for cable-stayed bridge. Journal of Structural Engineering, 45 (A), 443-451.
11. Furuta, H., Kameda, T., Fukuda, Y., and Frangopol, D.M., 2003. Life-cycle cost analysis for infrastructure systems: Life-cycle cost vs. safety level vs. service life. Life-cycle performance of deteriorating structures: Assessment, design and management. Reston, VA: ASCE, 19-25.
12. Horn, J., Nafploitis, N., and Goldberg, D.E., 1994. A niched Pareto genetic algorithm for multi-objective optimisation. In: Proceedings of the 1st IEEE conference on evolutionary computation, 27-29 June, Orlando, USA. New Jersey: IEEE, 82-87.
13. KICT (Korea Institute of Construction Technology), 2006. Development of life-cycle cost analysis method and system for the life-cycle cost optimum design and the life-time management of steel bridges. Research Report. MOCT and KICTTEP, Korea (in Korean).
14. Knowles, J.D. and Corne, D.W., 2000. Approximating the nondominated front using the Pareto archived evolutionary strategy. Evolutionary Computation, 8 (2), 149-172.
15. Liu, M., Hammad, A., and Itoh, Y., 1997. Multi-objective optimisation of bridge deck rehabilitation using a genetic algorithm. Computer-Aided Civil and Infrastructure Engineering, 12, 431-443.
16. Liu, M. and Frangopol, D.M., 2004. Optimal bridge maintenance planning based on probabilistic performance prediction. Engineering Structure, 26, 991-1002.
17. Liu, M. and Frangopol, D.M., 2005. Multi-objective maintenance planning optimisation for deteriorating bridges considering condition, safety, and life-cycle cost. Journal of Structural Engineering, 131 (5), 833-842.
18. Messac, A., 1996. Physical programming: effective optimisation for computational design. AIAA Journal, 34 (1), 149-158.
19. Messac, A. and Wilson, B.H., 1998. Physical programming for computational control. AIAA Journal, 36 (2), 219-226.
20. MOCT (Ministry of Construction and Transportation) and KISTEC (Korea Infrastructure Safety and Technology Corporation), 2003. Detailed guidelines of safety inspection and precise safety diagnosis. Research Report. MOCT and KISTEC, Korea (in Korean).
21. Miyamoto, A., Kawamura, K., and Nakamura, H., 2000. Bridge management system and maintenance optimisation for existing bridges. Computer-Aided Civil and Infrastructure Engineering, 15, 45-55.
22. Nakayama, H. and Furukawa, K., 1985. Satisficing trade-off method with an application to multi-objective structure design. Large Scale Systems, 8, 47-57.
23. Nakayama, H., Kaneshige, K., Takemoto, S., and Watada, Y., 1995. An application of a multi-objective programming technique to construction accuracy control of cable-stayed bridges. European Journal of Operational Research, 87, 731-738.
24. Ok, S.-Y., Song, J., and Park, K.-S., 2009. Development of optimal design formula for bi-tuned mass dampers using multi-objective optimisation. Journal of Sound and Vibration, 322 (24), 60-77.
25. Park, K.-S., Koh, H.-M., and Hahm, D., 2004. Integrated optimum design of viscoelastically damped structural systems. Engineering Structure, 26 (5), 581-591.
26. Park, K.-S. and Koh, H.-M., 2004. Preference-based optimum design of an integrated structural control system using genetic algorithms. Advances in Engineering Software, 35, 85-94.
27. Srinivas, N. and Deb, K., 1995. Multi-objective function optimisation using non-dominated sorting genetic algorithms. Evolutionary Computation, 2 (3), 221-248.
28. Zitzler, E. and Thiele, L., 1999. Multi-objective optimisation evolutionary algorithms: a comparative case study and the strength Pareto approach. Evolutionary Computation, 3 (4), 257-271.