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为解决在一定噪声水平影响下的结构损伤识别问题,提出一种基于频响函数和改进的粒子群算法的结构损伤识别方法。以单元刚度折减因子为优化变量,采用实测频响函数和计算频响函数的相关系数来构造粒子群算法(PSO)的优化目标函数和适应度函数;考虑到简单PSO算法在寻优过程易“早熟”的问题,采用增大粒子后期位置改变量的改进策略,最后通过该算法对IASC-ASCE SHM Benchmark结构进行损伤识别。结果表明:改进后的算法是有效的,且较简单PSO算法结果更精确,收敛速度更快。
In order to solve the problem of structural damage identification under the influence of certain noise level, a structural damage identification method based on frequency response function and improved particle swarm optimization is proposed. Taking the unit stiffness reduction factor as the optimization variable, the objective function and the fitness function of Particle Swarm Optimization (PSO) are constructed by using the correlation coefficients between the measured frequency response function and the calculated frequency response function. Considering the simple PSO algorithm, “Precocious ” problem, we adopt an improved strategy to increase the amount of change of particle position in the late stage. Finally, IASC-ASCE SHM Benchmark structure is identified by this algorithm. The results show that the improved algorithm is effective, and the results of the simpler PSO algorithm are more accurate and the convergence speed is faster.