改进分层遗传算法在斜拉桥主梁损伤识别中的应用

Application of Improved Hierarchic Genetic Algorithm to Damage Detection of the Main Girder for Cable-stayed Bridge

标准遗传算法在解决像斜拉桥这类复杂结构的损伤识别问题时会出现提前收敛,即所谓"早熟"的现象。为了避免此现象的发生,提高损伤识别的效率与精度,提出一种基于改进分层遗传算法的斜拉桥主梁损伤识别方法。采用索力变化作为优化目标函数,将3种具有不同遗传算子的标准遗传算法与变量微调和灾变策略相结合,形成了一种具有灾变特性的分层遗传算法,以实验室独塔斜拉桥模型作为研究对象进行了数值仿真,结果表明:改进的分层遗传算法成功的避免了标准遗传算法"早熟"现象的发生,能快速有效的完成斜拉桥主梁各种损伤的识别;同时对此方法进行抗噪性分析发现,该方法具有良好的抗噪能力。

In the process of damage identification for high‐order nonlinear structure such as cable‐stayed bridges by the standard genetic algorithm ,premature convergence would appear .In order to avoid this ,an improved hierarchic genetic algorithm was proposed . The cable force change was used to establish the optimization function and threetypes of standard genetic algorithm were combined with variable fine‐tuning and hierarchic strategy .To establish a hierarchical genetic algorithm with catastrophe characteristics A single‐tower cable‐stayed bridge model was used in the numerical simulation and the result showed that the probability of premature convergence was reduced in the improved hierarchic genetic algorithm and and the cable‐stayed bridge damage was identified effectively .The anti‐noise performance was better .