基于人工鱼群算法的结构模型修正与损伤检测

提出结构模型修正结构损伤检测的人工鱼群算法。将结构模型修正与结构损伤检测结构动力学逆问题转化为约束优化数学问题,并尝试用人工鱼群算法求解。介绍人工鱼群算法基本原理,定义关键参数并描述觅食、聚群、追尾及随机等行为;据模型修正原理利用结构损伤前后模态特性数据定义优化问题目标函数;通过两层刚架不同损伤工况数值仿真、三层框架试验数据验证方法的有效性。结果表明,基于人工鱼群算法的结构模型修正与损伤检测方法能有效修正结构有限元模型,在不同噪声水平及各种结构损伤工况下不仅能准确定位结构损伤且能精确识别损伤程度。

Structural model updating and damage detection based on artificial fish swarm algorithm

An artificial fish swarm algorithm (AFSA) based novel method is proposed for structural model updating and damage detection, which is often converted into a constrained optimization problem in mathematics and is hopefully solved by the AFSA proposed in this paper. The basic principle of AFSA is introduced, some key parameters defined and four fish swarm behaviors simulated simultaneously, including searching, swarm, chasing and random behaviors. An objective function is defined as minimizing the discrepancies between the experimental and analytical modal parameters (namely natural frequencies and mode shapes). One numerical two-story portal frame structure and one laboratory-tested three-story steel frame structure are both adopted to evaluate the efficiency of the proposed method. Some illustrated results show that the proposed AFSA based method can effectively update finite element models, locate damaged elements of structures and identify extents of structural damages under different noise levels and all the damage cases.