基于改进灰狼优化算法的结构损伤识别

结合模态柔度矩阵、广义模态柔度矩阵和振型三个识别精度较好的指标,构造新的目标函数求解损伤识别问题。通过Nelson方法求解得到的频率与振型的导数,得到对结构刚度发生变化时更具敏感性的位置,然后在这些位置布置传感器以提取结构信息。针对原有的灰狼算法虽然全局搜索能力强,但是存在局部搜索精度差的问题,本文从初始种群和收敛因子等方面着手,改善灰狼算法的局部搜索能力及收敛速度。最后利用提出的方法,通过识别梁模型及桁架模型中的损伤单元说明本文方法的有效性。

Structural damage identification based on improved gray wolf optimization algorithm

In this paper,a new objective function is constructed to solve the damage identification problem by combining three indicators with good identification accuracy:modal flexibility matrix,generalized modal flexibility matrix and mode shape.The derivative of a frequency and a mode shape obtained by Nelson method is used to obtain the positions that are more sensitive to the change of structural stiffness,and then sensors are arranged at these positions to extract structural information.Although the original gray wolf algorithm has strong global search ability,it has the problem of poor local search accuracy.This paper improves the local search ability and convergence speed of gray wolf algorithm from the aspects of initial population,convergence factor,etc.Finally,the effectiveness of the proposed method is demonstrated by identifying the damage elements in a beam model and a truss model.