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Multi-objective optimization design of bridge piers with hybrid heuristic algorithms

Multi-objective optimization design of bridge piers with hybrid heuristic algorithms
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摘要 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 CO 2 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. 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 algo- rithms (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.
出处 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2012年第6期420-432,共13页 浙江大学学报(英文版)A辑(应用物理与工程)
基金 supported by the Spanish Ministry of Science and Innovation(No. BIA2011-23602) the European Community with the European Regional Development Fund (FEDER), Spain
关键词 Bridge piers Concrete structures Multi-objective optimization Simulated annealing (SA) Structural design Bridge piers, Concrete structures, Multi-objective optimization, Simulated annealing (SA), Structural design
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  • 1Bailing, R.J., Yao, X., 1997. Optimization of reinforced concrete frames. ASCE Journal of Structural Engineering, 123(2):193-202. [doi:10.10611(ASCE)0733-9445(1997) 123:2(193)].
  • 2Bandyopadhyay, S., Saha, S., Maulik, U., Deb, K., 2008. A simulated annealing-based multi-objective optimization algorithm: AMOSA. IEEE Transactions on Evolutionary Computation, 12(3):269-283. [doi:]0.1109/TFVC.2007. 900837].
  • 3Carbonell, A., Gonzalez-Vidosa, E, Yepes, V., 2011. Design of reinforced concrete road vault underpasses by heuristic optimization. Advances in Engineering Software, 42(4): 151-159. [doi:l O. 1016/j.advengsoft.2011.01.002].
  • 4Catalonia Institute of Construction Technology, 2009. BEDEC PR/PCT ITEC Materials Database, Barcelona, Spain.
  • 5Cerny, V., 1985. Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. Journal of Optimization Theory and Applications, 45(1): 41-51. [Ooi:10.1007/BF00940812].
  • 6Coello, C.A., Christiansen, A.D., Santos, F., 1997. A simple genetic algorithm for the design of reinforced concrete beams. Engineering with Computers, 13(4):185-196. [doi:] 0.1007/BF01200046].
  • 7Cohn, M.Z., Dinovitzer, A.S., 1994. Application of structural optimization. ASCE Journal of Structural Engineering, 120(2):617-649. [doi:10.1061/(ASCE)0733-9445(1994) 120:2(617)].
  • 8Deb, D., 2001. Multi-Objective Optimization Using Evolu- tionary Algorithms. Wiley, New York, USA.
  • 9Dorigo, M., Maniezzo, V., Colomi, A., 1996. The ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 26(1):29-41. [doi: 10.1109/3477.484436].
  • 10Holland, J.H., 1975. Adaptation in Natural and Artificial Sys- tems. University of Michigan Press, Ann Arbor, USA.

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