基于遗传算法的二阶段结构损伤探测方法

为了有效地进行工程结构的损伤识别,提出了一种基于遗传算法的二阶段损伤探测方法.首先利用基于频率的多损伤定位准则和基于位移模态的多损伤定位准则分别计算出有关损伤的初步决策,然后利用信息融合技术中的证据理论方法,将两者的初步决策进行融合,从而获得较为精确的损伤位置估计,最后在已识别出的可能损伤单元的基础上,利用改进的遗传算法进行更精确的损伤位置和程度估计.数值仿真结果表明,采用证据理论进行融合可以获得较为精确的损伤位置估计,比单纯的多损伤定位准则识别效果更好,而采用改进的遗传算法则可以更为精确地判定损伤的程度,优于简单的遗传算法.

Two-Stage Structural Damage Detection Method with Genetic Algorithms

To efficiently identify structural damage, a two-stage damage detection method with genetic algorithm is presented. The frequency multiple damage location assurance criterion (MDLAC) method and mode shape MDLAC method are applied to obtain original decisions, respectively, and the evidence theory is employed to integrate both frequency identification information and mode shape identification information, which is more accurate than MDLAC methods, thus the damage sites are preliminarily localized according to the combined information. The improved genetic algorithm is finally applied to detect damage extents. The simulation results demonstrate that the evidence theory approach containing both frequencies and mode shapes can more successfully identify multiple damage sites or determine the group of potential damage sites than MDLAC methods, and the improved genetic algorithm can perfectly detect damage extents.