基于递阶遗传算法的结构多损伤监测

基于递阶遗传算法(HGA)与结构优化思想,提出了一种针对欧拉-伯努利梁和二维板结构的多损伤监测方法.该方法利用递阶遗传算法的控制基因表示损伤的数量和位置,以参数基因表示损伤的程度,有效地避免了传统遗传算法(CGA)的早熟现象所造成的损伤误识别等问题.一个悬臂梁和悬臂方板结构模型的多损伤监测仿真计算表明该方法能够准确地监测一、二维结构中多个位置的损伤,而传统遗传算法难以识别二维结构中的多损伤情况.悬臂梁仿真算例中,该方法和传统遗传算法对多损伤程度的识别误差分别为0.144%和1.819%,所需的有限元计算次数该方法仅为传统遗传算法的16.4%.与传统遗传算法相比,递阶遗传算法明显提高了损伤识别方法的计算效率、精度和稳定性. 

Multiple damages detection of structure based on hierarchical genetic algorithm

On the basis of a hierarchical genetic algorithm(HGA) and the idea of structural optimization,a method for multiple damages detection of an Euler-Bernoulli beam and a 2-D plate structure was presented.The algorithm employed control genes to represent the number and location of multiple damages and parametric genes to denote damage extents, effectively avoiding the premature phenomenon of conventional genetic algorithm(CGA).The results of a cantilever beam and a 2-D cantilever plate simulation examples show that the good agreements with the actual situation can be obtained by using the present method,but CGA can't accurately predict multiple damages of the plate.For the simulation of the cantilever beam,the average values of depth prediction errors for HGA and CGA are 0.144% and 1.819%,respectively. The iteration numbers of finite element computation of HGA are only 16.4% of that of CGA.Compared with CGA,HGA remarkably improves the computational efficiency,accuracy and stability.