改进粒子群算法在桥梁结构损伤识别传感器优化布设中的应用
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摘要
粒子群算法以其强大的搜索能力及较好的适应度已逐步应用到桥梁结构损伤识别领域。针对以损伤识别为目标的传感器优化布设,提出一种基于改进粒子群算法的传感器优化布置方法。首先利用传感器覆盖率概念建立数学模型;其次以联合覆盖率最大构造适应度函数;在确定了监测半径的条件下,利用粒子群算法寻找出传感器的最优数目与位置。为了验证所提方法的有效性,以一三跨桥梁结构为实验对象。实验结果表明,改进的粒子群算法相比于传统粒子群算法,在寻优过程中迭代次数更少,寻找到的最优值更好,且经过融合后的数据损伤识别结果更加真实可靠。
The particle swarm optimization algorithm with strong search ability and perfect robustness is gradually applied to the field of bridge structure damage identification. A sensor optimal placement method based on improved particle swarm optimization(PSO) algorithm is proposed for the sensor optimal placement taking damage identification as the object. The concept of sensor coverage rate is used to establish the mathematical model,and then the maximum joint coverage rate is used to construct the fitness function. After the determination of the monitoring radius,the PSO algorithm is adopted to find out the optimal number and location of sensors. In order to verify the effectiveness of the proposed method,a three-span bridge structure is taken as the experimental object. The experimental results show that,in comparison with the traditional PSO algorithm,the improved PSO algorithm has less iteration number in the optimization process,better optimal value,and more authentic and reliable damage recognition results after data fusion.
The particle swarm optimization algorithm with strong search ability and perfect robustness is gradually applied to the field of bridge structure damage identification. A sensor optimal placement method based on improved particle swarm optimization(PSO) algorithm is proposed for the sensor optimal placement taking damage identification as the object. The concept of sensor coverage rate is used to establish the mathematical model,and then the maximum joint coverage rate is used to construct the fitness function. After the determination of the monitoring radius,the PSO algorithm is adopted to find out the optimal number and location of sensors. In order to verify the effectiveness of the proposed method,a three-span bridge structure is taken as the experimental object. The experimental results show that,in comparison with the traditional PSO algorithm,the improved PSO algorithm has less iteration number in the optimization process,better optimal value,and more authentic and reliable damage recognition results after data fusion.
引文
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[12]赵建华,张陵,孙清.利用粒子群算法的传感器优化布置及结构损伤识别研究[J].西安交通大学学报,2015,49(1):79-85.ZHAO Jianhua, ZHANG Ling, SUN Qing. Optimal place-ment of sensors for structural damage identification using im-proved particle swarm optimization[J]. Journal of Xi’an Jiao-tong University,2015,49(1):79-85.
[2] UDWADIA F E. Methodology for optimal sensor locations for parameter identification in dynamic systems[J]. Journal of engi-neering mechanics,1994,120(2):368-390.
[3] KAMMER D C. Sensor placement for on-orbit modal identifica-tion and correlation of large space structures[J]. Journal of guidance,control and dynamics,1991,14(2):251-259.
[4] MOORE E Z,MURPHY K D,NICHOLS J M. Optimized sen-sor placement for damage parameter estimation:experimental results for a cracked plate[J]. Structural health monitoring,2013,12(3):197-206.
[5]孙小猛,冯新,周晶.基于损伤可识别性的传感器优化布置方法[J].大连理工大学学报,2010,50(2):264-270.SUN Xiaomeng,FENG Xin,ZHOU Jing. A method for opti-mum sensor placement based on damage identifiability[J]. Journal of Dalian University of Technology,2010,50(2):264-270.
[6]刘寒冰,吴春利,程永春.不同适应度函数的遗传算法在桥梁结构传感器布设中的应用[J].吉林大学学报,2012,42(1):51-56.LIU Hanbing,WU Chunli,CHENG Yongchun. Sensor place-ment on bridge structure based on genetic algorithms with dif-ferent fitness functions[J]. Journal of Jilin University,2012,42(1):51-56.
[7]周丹.基于量子微粒子群优化算法的非线性观测器研究[D].无锡:江南大学,2007.ZHOU Dan. Research on nonlinear observation based quantum particle swarm optimization algorithm[D]. Wuxi:Jiangnan Uni-versity,2007.
[8] KENNEDY J,EBERHART R C. Particle swarm optimization[C]//Proceedings of 1995 IEEE International Conference on Neural Networks. Perth:IEEE,1995:1942-1948.
[9] SHI Y,EBERHART R C. A modified particle swarm optimizer[C]//Proceedings of 1998 IEEE International Conference on Evolutionary Computation. Anchorage:IEEE,1998:69-73.
[10] SHI Y,EBERHART R C. Empirical study of particle swarm optimization[C]//Proceedings of 1999 Congress on Evolutio-nary Computation. Washington,DC:IEEE,1999:1945-1950.
[11]马慧民.生产批量计划问题的粒子群算法研究[D].上海:上海理工大学,2006.MA Huimin. Study on particle swarm optimization algorithm for dynamic lot-sizing problem[D]. Shanghai:University for Shanghai of Science and Technology,2006.
[12]赵建华,张陵,孙清.利用粒子群算法的传感器优化布置及结构损伤识别研究[J].西安交通大学学报,2015,49(1):79-85.ZHAO Jianhua, ZHANG Ling, SUN Qing. Optimal place-ment of sensors for structural damage identification using im-proved particle swarm optimization[J]. Journal of Xi’an Jiao-tong University,2015,49(1):79-85.