智能优化算法在结构损伤识别中的应用
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摘要
利用智能优化算法进行结构的损伤识别是近年来的研究热点,其基本思路是将实测数据和数值模型的输出定义为结构参数的目标函数,通过搜索目标函数的最小值来得到结构参数,并根据这些参数在损伤前后的变化情况来识别损伤。目标函数可以有多种定义方式,智能优化算法也有多种算法可以选择,为了得到最佳的计算效果,利用4种目标函数和3种智能优化算法进行了损伤识别的数值模拟。计算结果表明,由加速度时程响应定义的目标函数与差分进化算法相结合具有很好的识别精度和抗噪能力。
The structural damage detection in use of intelligent optimization algorithm has been a hot research topic recently.Its basic idea is that measured response and calculated response are defined as objective function of structural parameters,then the structural parameters are obtained by minimizing the objective function,and then the changes of the parameters before and after damage can be used to detect the damage.As the objective function can be defined in terms of many formulations and the intelligent optimization algorithm can also be chosen from many types of algorithms,in order to obtain the optimum calculation results,four objective functions and three intelligent optimization algorithms were used for the numerical simulations of damage detection.The results show that the combination of the objective function defined by acceleration history response and DE algorithm has high accuracy of detection and well ability in anti noise.
The structural damage detection in use of intelligent optimization algorithm has been a hot research topic recently.Its basic idea is that measured response and calculated response are defined as objective function of structural parameters,then the structural parameters are obtained by minimizing the objective function,and then the changes of the parameters before and after damage can be used to detect the damage.As the objective function can be defined in terms of many formulations and the intelligent optimization algorithm can also be chosen from many types of algorithms,in order to obtain the optimum calculation results,four objective functions and three intelligent optimization algorithms were used for the numerical simulations of damage detection.The results show that the combination of the objective function defined by acceleration history response and DE algorithm has high accuracy of detection and well ability in anti noise.
引文
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[2]薛祥,霍达,滕海文.基于改进遗传算法的公路桥梁损伤程度标定的两阶段法[J].世界地震工程,2006,22(3):60-65.XUE Xiang,HUO Da,TENG HAI-Weng.The two stage method of the damage identification of highway bridge based on improved genetic algo-rithms[J].World Earthquake Engneering,2006,22(3):60-65.
[3]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.WANG Jianyou,CHEN Jianyun,HUANG Jingzhong.Identification for structural parameters based on limited modal information[J].World Earth-quake Engneering,2008,24(3):45-48.
[4]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(05),37-40.YU ling,WAN Zuyong,ZHU Hongping,Yu Deyi.Model updating and damage detection based on PSO[J].Journal of Vibration and Shock,2006,25(05),37-40.
[5]TANG Hesheng,XUE Songtao and FAN Cunxin.Differential evolution strategy for structural system identification[J].Computers and Structures,2008,86(21-22):2004-2012.
[6]汪定伟.智能优化算法[M].北京:高等教育出版社,2007.WANG Dingwei.Intelligent optimization algorithm[M].Beijing:Higher Education Press.
[7]Bagley J.D..The behavior of adaptive systems which employ genetic and correlation algorithms[D].US:University of Michigan,Ann Arbor,1967.
[8]Holland J.H..Adaptation in Natural and Artificial Systems[M].UN:MIT Press,1975.
[9]Kennedy J.and Eberhart R.C..Particle Swarm Optimization[C].Proceedings of IEEE International Conference on Neural Networks,Australia,Perth,1995,1942-1948.
[10]Parsopoulous K.E.and Vrahatis M.N.Particle swarm optimization method in multimobjective problems[C].Proc ACM SAC,Madrid,Spain,2002,603-607.
[11]Storn R.and Price K..Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces[C].Techni-cal report International Computer Science Institute,Berkley,1995.
[12]Price K..Differential evolution vs.the functions of the2nd ICEO[C].IEEE International Conference on Evolutionary Computation.Indianapolis,1997,153-157.
[13]Vesterstr?m J.and Thomsen R..A comparative Study of Differential Evolution,Particle Swarm Optimization,and Evolutionary Algorithms on Numerical Benchmark Problems[C].Congress on Evolutionary Computation,Denmark,2004,2:1980-1987.
[14]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,(03):86-89.YU Youming,LIU Yushu and YAN Guangwei.Encoding theory and application of Genetic Algorithm[J].Computer Engineering and Applica-tions,2006,(03):86-89.
[15]陈昆,石国桢.浮点数编码遗传算法变异概率的选取[J].武汉理工大学学报(交通科学与工程版),2001,25(4):496-499.CHEN Kun and SHI Guozhen.Selection of mutation probability of floating-point number code Genetic Algorithm[J].Journal of Wuhan Universi-ty of Techonology(Transportation Science&Engineering),2001,25(4):496-499.
[16]Carlisle A.and Dozier G.An Off-The-Shelf PSO[C].Proc.of the Particle Swarm Optimization Workshop,Indianapolis,2001:1-6.
[17]Eberhart R.C.and Shi Y..Comparing inertia weights and constriction factors in Particle Swarm Optimization[C].Proc.of the Congress on Evo-lutionary Computating,Belgium,2000:84-88.
[18]Kennedy J..The Particle Swarm:Social Adaptation of Knowledge[C].IEEE International Conference on Evolutionary Computation,US,1997:303-308.
[19]R nkk nen J.,Kukkonen S.and Price K.V.,Real-parameter optimization with differential evolution[C].Proc.of Congress on Evolutionary Computation,IEEE press,2005:506-513.
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