Structural Damage Identification Based on Variable-Length Elements and an Improved Genetic Algorithm for Railway Bridges

Abstract

A new damage identification method is proposed to solve the problem of no correspondence between the element division form of the finite element model and the actual damage location. The three basic operators in the traditional genetic algorithm are improved, and the catastrophe and neighborhood search processes are introduced to enhance the local optimization ability of the algorithm. The train–rail–bridge coupling time-varying equation is established. Based on the dynamic response of the bridge under trainload, the damage index is constructed, and the corresponding objective function is given. Through a numerical example, the stability and convergence rate of the algorithm are statistically analyzed. The effects of noise, the number of measuring points, and train speed on the recognition results are discussed. The research results indicate that, even if the damage location is different from the element division form of the finite element model, this method can accurately locate the damage location, but it will affect the quantitative results to a certain extent. In addition, the convergence speed of this method is fast, and the computing efficiency is about 6.7 times that of the conventional one-time recognition method.