基于互相关函数的聚类蝴蝶优化算法的结构损伤识别

Structural damage identification based on clustering butterfly optimization algorithm of cross-correlation function

针对多点未知随机激励下仅利用输出响应识别结构损伤的问题,提出基于互相关函数的聚类蝴蝶优化算法的结构损伤识别方法。首先将结构损伤考虑为刚度和质量同时损伤,并根据测量和计算得到的加速度互相关函数建立目标函数,并采用所提出的聚类蝴蝶优化算法进行损伤识别。通过简支梁和剪切型钢框架两个数值模拟算例验证方法的有效性,结果表明,多点未知随机激励下使用有限测量数据,基于互相关函数的聚类蝴蝶优化算法的结构损伤识别方法不仅可以精确识别结构的多处损伤,而且具有较好的噪声鲁棒性。

Here,aiming at the problem of only using output response to identify structural damage under multiple unknown random excitations,a new structural damage identification method using the clustering butterfly optimization algorithm based on cross-correlation function was proposed.The structural damage was considered as simultaneous reduction of mass and stiffness,the cross-correlation function of structural acceleration obtained with measuring and calculating was used to establish the objective function,and the proposed clustering butterfly optimization algorithm was used to do damage recognition.The effectiveness of the proposed method was verified with two numerical simulation examples of a simply-supported beam and a shear type steel frame.The results showed that under multiple unknown random excitations,the structural damage identification method with the clustering butterfly optimization algorithm based on cross-correlation function can not only accurately detect multiple structural damages using finite measured data,but also have better noise robustness.