Study on Finite Element Model Modification of Long-Span Suspension Bridge Based on BPANN-GA

Abstract

In order to improve the reliability of the finite element analysis model of long-span suspension bridges, this paper proposes a finite element model (FEM) modification method by the hybrid algorithm of backpropagation artificial neural network (BPANN) and genetic algorithm (GA) based on field measurements and vibration modal analysis. First, finite element computational data is used to train the neural network. The trained neural network is then used to predict the natural frequencies corresponding to different modal shapes under various parameters. Based on the measured values, a fitness function is constructed, and the GA is used to optimize the model parameters. These optimized parameters are subsequently applied to correct the FEM of long-span suspension bridges. Finally, the computational errors of the initial model, the BPANN corrected model, and the BPANN-GA model are compared and analyzed to verify the advantages of using BPANN-GA for bridge model modification. The results show that the average computational error of the natural frequencies for the 1st to 8th modes before modification is 7.04%. After modification using BPANN-GA, the absolute value of the computational error for the 1st to 8th modes is all below 3%, and the modal assurance criterion (MAC) values all exceed 90%. Compared to the conventional BPANN, BPANN-GA can effectively improve the modification effect, with the average computational errors of natural frequencies being 3.84% and 1.50%, respectively. For static displacement, the computational error after modification is also significantly reduced, with the average computational error decreasing from 11.4% to 5.9%. After modification using BPANN-GA, the static and dynamic responses of the structure can be better reflected by the corrected model, thus providing a more reliable computational basis for understanding the safety status of the bridge.