Model Updating for a Railway Bridge Using a Hybrid Optimization Algorithm Combined with Experimental Data

This paper proposes a hybrid optimization algorithm combining particle swarm optimization (PSO) with genetic algorithm (GA) to update a railway bridge. PSO is an evolutionary optimization algorithm based on global search techniques to look for the best solution. Nevertheless, since PSO relies crucially on the quality of initial particles, it may reduce its effectiveness and robustness in tacking optimization issues. If the positions of initial populations are too far from the global best, it is challenging to determine the best solution. To overcome these shortcomings, we propose a hybrid optimization algorithm applying the advantages of both PSO and GA. GA is first used to generate the most elite populations based on its crossover and mutation characteristics. Those populations are then employed to seek the best solution based on the global search capacity of PSO. The experimental measurements of the railway bridge are carried out under ambient vibrations used to validate the proposed algorithm (PSO-GA). The result demonstrates that PSO-GA, GA, and PSO possibly determine uncertain parameters of the bridge exactly and PSO-GA surpasses GA alone and PSO alone in terms of convergence level and accuracy.