基于CACO算法的结构多损伤识别

A CACO-based algorithm for structural multi-damage identification

结构多损伤识别是结构健康监测领域的一个具有挑战性的研究课题,数学上,它通常可以转化为连续函数的约束优化问题。介绍连续优化蚁群算法(CACO)的基本原理、从离散ACO到CACO的实现、寻优路径的构建、以及信息素更新和挥发的数学建模,尝试探寻将CACO算法应用于结构多损伤识别问题的可行性并进行数值仿真和实验研究。通过两层刚架多损伤数值仿真以及三层建筑框架结构4种损伤的实验研究,结果表明:采用CACO算法对结构多损伤进行识别不但能够准确定位结构多损伤,而且还可以有效识别其损伤程度。由此可见,CACO算法应用于结构多损伤识别的效果是显而易见的。

Structural multi-damage detection remains as a challenging task in the field of structural health monitoring（SHM）.Mathematically,it is often converted into some kind of constrained optimization problems on continuous functions.In this paper,a theoretical background on continuous ant colony optimization（CACO） is introduced from discrete ACO to CACO models,sampling processes of ant paths are built,ant pheromone updating and evaporation models are also established in mathematics.As a new exploring attempt to structural damage detection,the CACO algorithm is applied to continuous optimization problem on structural damage detection in the SHM field.Based on numerical simulations about multiple damages of a 2-story rigid frame and on experimental study on damage detection of a 3-story building model in laboratory,some illustrated results show that the CACO algorithm is feasible and effective in structural damage detection.The CACO algorithm can not only locate accurate positions of multiple damages but also quantify severity of structural damages.