基于RBFNN-ISSA的特大跨径悬索桥有限元模型修正

王祺顺1,2, 何维3, 吴欣1,2,郭伟奇1,3,雷顺成1,2

振动与冲击 ›› 2024, Vol. 43 ›› Issue (7) : 155-167.

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振动与冲击 ›› 2024, Vol. 43 ›› Issue (7) : 155-167.
论文

基于RBFNN-ISSA的特大跨径悬索桥有限元模型修正

  • 王祺顺1,2, 何维3, 吴欣1,2,郭伟奇1,3,雷顺成1,2
作者信息 +

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
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摘要

针对大跨径悬索桥一类复杂结构的有限元模型修正问题,提出了一种基于径向基神经网络(Radial Basis Function Neural Network,RBFNN)子结构代理模型与改进麻雀搜索算法(Improved Sparrow Search Algorithm, ISSA)的有限元模型修正方法。首先,基于桥梁图纸数据采用通用有限元软件建立一座大跨悬索桥的初始有限元模型,并根据拉丁超立方抽样原则生成子结构材料参数-结构响应的训练样本,通过RBF神经网络和子结构模拟方法对初始有限元模型进行解构重组和样本学习,拟合关于材料参数-结构响应的代理模型。其次,建立考虑主梁挠度和模态频率误差最小的有限元模型参数修正数学优化模型,采用Tent混沌映射及黄金正弦策略改进标准麻雀搜索算法,引入柯西分布函数和贪心保留策略对每一代麻雀种群进行扰动,以用于求解联合静、动力特征的有限元模型修正数学优化问题。最后,以杭瑞高速洞庭湖大桥为工程背景,进行了悬索桥荷载试验,利用实测桥梁响应数据验证了该方法的可行性。研究结果表明:本文提出的基于RBF神经网络与子结构法的模型修正方法,可以建立拟合精度较高的悬索桥结构代理模型;基于子结构RBF神经网络与改进麻雀搜索算法修正后的有限元模型相较于整体RBF神经网络、支持向量机和Kriging模型,大幅提升了对于实际结构的模拟精度,与实测数据相比,修正前后有限元模型在两级静力加载工况下13个有效测点挠度的平均相对误差降低了25%以上,前8阶模态频率的平均相对误差由-6.83%降至-2.38%,MAC值结果表明修正后模型能够准确的反映出大桥的的实际振动状态,有效改善了初始有限元模型计算失真的情况;此外,基于混合策略改进后的麻雀搜索算法对于有限元模型修正参数的寻优具有更佳的收敛效率和稳定性。

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..

关键词

桥梁工程 / 有限元模型修正 / 麻雀搜索算法 / 悬索桥 / 径向基神经网络 / 柯西变异策略

Key words

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

引用本文

导出引用
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[J]. Journal of Vibration and Shock, 2024, 43(7): 155-167

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