Finite element model correction of super long-span suspension bridge based on RBFNN-ISSA

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (7) : 155-167.

WANG Qishun1,2, HE Wei3, WU Xin1,2, GUO Weiqi1,3, LEI Shuncheng1,2

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Abstract

Aiming at the problem of finite element model updating for a class of complex structures of long-span suspension bridges, a finite element model updating method based on radial basis function neural network (RBFNN) substructure surrogate model and improved sparrow search algorithm (ISSA) is proposed. Firstly, the initial finite element model of a long-span suspension bridge is established by using the general finite element software based on the bridge drawing data, and the training samples of the material parameter structure response of the substructure are generated according to the Latin hypercube sampling principle. The initial finite element model is deconstructed and reconstructed by RBF neural network and substructure simulation method, and the surrogate model of material parameter structure response is fitted. Secondly, the finite element model parameter correction mathematical optimization model considering the minimum deflection and modal frequency error of the main beam is established. The standard sparrow search algorithm is improved by using tent chaotic mapping and golden sine strategy, and the Cauchy distribution function and greedy retention strategy are introduced to disturb each generation of sparrow population to solve the finite element model correction mathematical optimization problem with combined static and dynamic characteristics. Finally, taking Yueyang Hangrui Dongting Lake Bridge as the engineering background, the load test of suspension bridge is carried out, and the feasibility of this method is verified by the measured bridge response data. The results show that the model updating method based on RBF neural network and substructure method proposed in this paper can establish a suspension bridge structure surrogate model with high fitting accuracy; Compared with the standard RBF neural network, support vector machine and Kriging model, the modified finite element model based on substructure RBF neural network and improved sparrow search algorithm significantly improves the simulation accuracy of the actual structure. Compared with the measured data, the relative errors of the theoretical deflection values of 13 effective measurement points of the modified finite element model under two-stage static loading conditions are reduced by more than 25%, The average relative error of the first eight modal frequencies decreased from -6.83% to -2.38%. The MAC value results verified that the modified model could accurately reflect the actual vibration state of the bridge, and effectively improved the calculation distortion of the initial finite element model; In addition, the improved sparrow search algorithm based on hybrid strategy has better convergence efficiency and stability for the optimization of finite element model correction parameters..

Key words

Bridge engineering / Finite element model correction / Sparrow search algorithm / Suspension bridge / Radial basis function neural network / Cauchy mutation strategy

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WANG Qishun1,2, HE Wei3, WU Xin1,2, GUO Weiqi1,3, LEI Shuncheng1,2. Finite element model correction of super long-span suspension bridge based on RBFNN-ISSA[J]. Journal of Vibration and Shock, 2024, 43(7): 155-167

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