Finite element model correction of super long-span suspension bridge based on RBFNN-ISSA
WANG Qishun1,2, HE Wei3, WU Xin1,2, GUO Weiqi1,3, LEI Shuncheng1,2
1. Hunan Key Laboratory of Transportation Construction Engineering, Hunan Transportation Science Research Institute Co., Ltd., Changsha 410015， China;
2. Key Laboratory of Bridge Engineering Safety Control, Provincial and Ministry of Education, School of Civil Engineering, Changsha University of Technology, Hunan 410015, China；
3. Hunan Key Laboratory of Wind Engineering and Bridge Engineering, College of Civil Engineering, Hunan University, Changsha 410082, China
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..
王祺顺1,2, 何维3, 吴欣1,2，郭伟奇1,3，雷顺成1,2. 基于RBFNN-ISSA的特大跨径悬索桥有限元模型修正[J]. 振动与冲击, 2024, 43(7): 155-167.
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. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(7): 155-167.
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