基于遗传算法的两阶段铁路桥梁结构损伤识别

采用智能优化算法对结构进行损伤识别时,通常假定实际损伤位置和有限元模型单元划分形式一致。为研究当两者之间存在差异时,对其识别效果的影响,提出了一种新的损伤识别方法。对传统遗传算法中的选择、交叉及变异3种算子进行了改进,并引入灾变和邻域搜索机制。通过一个工程算例对不同损伤工况进行多次独立重复计算,对算法的稳定性和收敛速度进行了分析。此外,还讨论了噪声、测点数目和列车速度对识别结果的影响。结果表明：即使损伤位置与有限元模型的单元划分形式不同,所提方法也能准确定位损伤位置,但会在一定程度上影响定量结果;所提方法收敛速度快,计算效率高于传统的一次识别方法。

Two-stage Damage Identification for Raiway Bridge structures Based on Genetic Algorithm

When using an intelligent optimization algorithm to identify structural damage, the actual damage location is usually assumed to be consistent with the division form of finite element model elements. In order to study the influence on the recognition effect when there is a difference between them, a new damage identification method is proposed. The traditional genetic algorithm"s selection, crossover, and mutation operators are improved, and the catastrophe and neighborhood search mechanism is introduced. Through an example, several independent repeated calculations are carried out for different damage conditions, and the stability and convergence speed of the algorithm is statistically analyzed. In addition, the effects of noise, the number of measuring points, and train speed on the recognition results are also discussed. The results show that even if the damage location is different from the element division form of the finite element model, this method can locate the damage location accurately, but it will affect the quantitative results to a certain extent, and the convergence speed of this method is fast. The computational efficiency is higher than that of the traditional primary identification method.