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基于改进遗传算法的公路桥梁损伤程度标定的两阶段法 被引量:1

The two stage method of the damage identification of highway bridge based on improved genetic algorithms
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摘要 提出了基于改进遗传算法的公路桥梁损伤程度标定的两阶段法。第一阶段:应用静应变残差进行损伤定位;第二阶段:基于已经识别出的损伤位置,利用改进的遗传算法进行损伤程度的标定。两阶段方法有效地克服了同时进行常规的损伤位置识别和损伤程度的标定的收敛速度慢、存储空间大及可能误标定等问题。某三跨连续桥梁应用分析发现,在已知很少实测数据的情况下,对损伤程度的识别取得较理想的效果,证实了基于改进遗传算法的两阶段法用于损伤程度的识别具有更高的效率,更好的灵敏度、稳定性和可靠性。 A new approach based on the detected position and the improved genetic algorithms is developed, namely the two stage method. It overcomes the shortcomings of the normal method, for instance, the slow convergence, requiting large memory space and mistake identification, and so on, in which the structural damage localization and identification are carried into execution at the same time. The numerical example for 3-span-girder reinforced con- crete bridge showc that the damage magnitude identification of the bridge is accurate even if only the dead displacement data are measured, and that the method has high efficiency, sensitivity, stability and reliability of the damage extent identification.
出处 《世界地震工程》 CSCD 北大核心 2006年第3期60-65,共6页 World Earthquake Engineering
基金 国家自然科学基金资助项目(50378007)
关键词 损伤程度 两阶段法 改进的遗传算法 公路桥梁 damage extent two stage method improved genetic algorithms highway bridge
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  • 1王建有,陈健云,黄景忠.基于有限测点模态信息的结构物理参数识别[J].世界地震工程,2008,24(3):45-48. 被引量:1
  • 2余有明,刘玉树,阎光伟.遗传算法的编码理论与应用[J].计算机工程与应用,2006,42(3):86-89. 被引量:59
  • 3余岭,万祖勇,朱宏平,徐德毅.基于POS算法的结构模型修正与损伤检测[J].振动与冲击,2006,25(5):37-39. 被引量:14
  • 4汪定伟,王俊伟,汪洪峰,张瑞友,郭哲.智能优化算法[M].北京:高等教育出版社,2007:26-40. 被引量:42
  • 5R.I. Levin and N. A.J. Lieven. Dynamic: finite element model updating using simulated annealing and genetic algorithm [ J]. Mechanical System and Signal Processing, 1998, 12( 1 ) :91 -120. 被引量:1
  • 6TANG Hesheng, XUE Songtao and FAN Cunxin. Differential evolution strategy for structural system identification [ J ]. Computers and Structures, 2008, 86(21 -22) : 2004 -2012. 被引量:1
  • 7Bagley J. D.. The behavior of adaptive systems which employ genetic and correlation algorithms [ D]. US: University of Michigan, Ann Arbor,1967. 被引量:1
  • 8,lland J. H.. Adaptation in Natural and Artificial Systems[ M]. UN:MIT Press, 1975. 被引量:1
  • 9Kennedy J. and Eberhart R. C.. Particle Swarm Optimization [ C ]. Proceedings of IEEE International Conference on Neural Networks, Australia, Perth, 1995, 1942-1948. 被引量:1
  • 10Parsopoulous K. E. and Vrahatis M. N. Particle swarm optimization method in muhimobjective problems[C]. Proc ACM SAC, Madrid, Spain, 2002, 603 - 607. 被引量:1

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