Multi-axle Moving Train Loads Identification on Simply Supported Bridge By Using Simulated Annealing Genetic Algorithm

Identification of multi-axle moving train loads is very important for the bridge design, construction, and maintenance. This paper presents an optimization method for identification of multi-axle moving train loads on bridges, which minimizes differences between the measured deflections and the deflections reconstructed from the identified moving loads and has the merits of both the global searching properties of genetic algorithms and the local searching properties of simulated annealing algorithms. Effects of algorithm parameters and of bridge-train system parameters on the robustness and accuracy of the proposed method are investigated. The results show that the method is feasible, accurate, and effective for multi-axle moving train loads identification.