Multi-objective optimization design of bridge piers with hybrid heuristic algorithms
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This paper describes one approach to the design of reinforced concrete (RC) bridge piers, using a three-hybrid multi-objective simulated annealing (SA) algorithm with a neighborhood move based on the mutation operator from the genetic algorithms (GAs), namely MOSAMO1, MOSAMO2 and MOSAMO3. The procedure is applied to three objective functions: the economic cost, the reinforcing steel congestion and the embedded CO2 emissions. Additional results for a random walk and a descent local search multi-objective algorithm are presented. The evaluation of solutions follows the Spanish Code for structural concrete. The methodology was applied to a typical bridge pier of 23.97 m in height. This example involved 110 design variables. Results indicate that algorithm MOSAMO2 outperforms other algorithms regarding the definition of Pareto fronts. Further, the proposed procedure will help structural engineers to enhance their bridge pier designs.
Key wordsBridge piers Concrete structures Multi-objective optimization Simulated annealing (SA) Structural design
CLC numberTU37 TP391
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- Catalonia Institute of Construction Technology, 2009. BEDEC PR/PCT ITEC Materials Database, Barcelona, Spain.Google Scholar
- Holland, J.H., 1975. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, USA.Google Scholar
- Kennedy, J., Eberhart, R., 1995. Particle Swarm Optimization. IEEE International Conference on Neural Networks, Perth, Australia. IEEE Service Center, Piscataway, p.1942–1948.Google Scholar
- Ministerio de Fomento, 1998. IAP-98: Code on the Actions to be Considered for the Design of Road Bridges. Madrid, Spain (in Spanish).Google Scholar
- Ministerio de Fomento, 2008. EHE-08: Code of Structural Concrete. Madrid, Spain (in Spanish).Google Scholar
- Neville, A.M., 1981. Properties of Concrete, 3rd Edition. Pitman, London, UK.Google Scholar
- Serafini, P., 1992. Simulated Annealing for Multiple Objective Optimization Problems. Proceedings of the Tenth International Conference on Multiple Criteria Decision Making, Taipei, p.87–96.Google Scholar