基于RBFNN-ISSA的特大跨径悬索桥有限元模型修正

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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

Received: 13 June 2023 Published: 15 April 2024

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	Articles by authors
	WANG Qishun1
	2
	HE Wei3
	WU Xin1
	2
	GUO Weiqi1
	3
	LEI Shuncheng1
	2

Cite this article:

WANG Qishun1,2,HE Wei3, et al. 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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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2024/V43/I7/155

[1] 梁鹏,赵云鹏,邬刚柔等.基于加权Kendall相关系数和序贯代理模型的有限元模型修正[J].中国公路学报,2021,34(12):57-67. Liang P, Zhao Y P, Hu G R, et al. Finite element model modification based on weighted Kendall correlation coefficient and sequential proxy model [J]. China Journal of Highway, 2021,34(12): 57-67. [2] 王一航, 彭珍瑞. 基于Kriging模型和分层模型修正技术的结构边界条件识别[J]. 振动与冲击, 2023, 42(06):134-142. Wang Y H, Peng Z R. Structural boundary condition identification based on Kriging model and hierarchical model correction technology [J] Vibration and Shock, 2023, 42 (06): 134-142 [3] 卢彭真,李登国,石擎天等.基于动载试验的变截面桥梁静载试验结果预测方法研究[J].中国公路学报,2022,35(08):213-221. Lu P Z, Li D G, Shi Q T, et al. Research on the prediction method of static load test results of variable cross-section bridges based on dynamic load tests [J]. Journal of China Highway Engineering, 2022,35 (08): 213-221 [4] Wu J, Yan Q, Huang S, et al. Finite Element Model Updating in Bridge Structures Using Kriging Model and Latin Hypercube Sampling Method[J]. Advances in Civil Engineering, 2018, 2018:1-11. [5] 胡俊亮,颜全胜,郑恒斌等.基于Kriging模型的钢管混凝土连续梁拱桥有限元模型修正[J].振动与冲击,2014,33(14):33-39. Hu J L, Yan Q S, Zheng H B, et al. Finite element model modification of steel tube concrete continuous beam arch bridge based on Kriging model [J]. Vibration and Impact, 2014,33 (14): 33-39 [6] Jaishi B, Ren W X, Jaishi B, et al. Structural Finite Element Model Updating Using Ambient Vibration Test Results[J]. Journal of Structural Engineering, 2005, 131(4):617-628. [7] 刘才玮,赵元元,黄绪宏等.基于动力测试的简支梁模型修正与参数分析[J].振动.测试与诊断,2022,42(02):394-401+414. Liu C W, Zhao Y Y, Huang X H, et al. Model modification and parameter analysis of simply supported beams based on dynamic testing [J]. Vibration, Testing and Diagnosis, 2022,42 (02): 394-401+414 [8] 刘纲,罗钧,秦阳等.基于改进MCMC方法的有限元模型修正研究[J].工程力学,2016,33(06):138-145. Liu G, Luo J, Qin Y, et al. Research on Finite Element Model Modification Based on Improved MCMC Method [J]. Engineering Mechanics, 2016,33 (06): 138-145 [9] 滕军,朱焰煌,卢云军等.基于多输出支持向量回归机的有限元模型修正[J].振动与冲击,2010,29(03):9-12+47+200. Teng J, Zhu Y H, Lu Y J, et al. Finite element model modification based on multi output support vector regression machine [J]. Vibration and Shock, 2010,29 (03): 9-12+47+200 [10] 张勇,侯之超,赵永玲.基于频响函数截断奇异值响应面的有限元模型修正[J].振动工程学报,2017,30(03):341-348. Zhang Y, Hou Z C, Zhao Y L. Finite element model modification based on frequency response function truncated singular value response surface [J]. Journal of Vibration Engineering, 2017,30 (03): 341-348 [11] 单德山,丁德豪,李乔等.基于RBF神经网络的单塔斜拉桥模型修正[J].重庆交通大学学报(自然科学版),2013,32(04):555-559+580. Shan D S, Ding D H, Li Q, et al. Model modification of single tower cable-stayed bridge based on RBF neural network [J]. Journal of Chongqing Jiaotong University (Natural Science Edition), 2013,32 (04): 555-559+580 [12] 盛腾威,殷红,彭珍瑞等.基于JS-BP模型和JS散度的随机有限元模型修正[J].噪声与振动控制,2022,42(01):41-47. Sheng T W, Yin H, Peng Z R, et al. Stochastic finite element model correction based on JS-BP model and JS divergence [J]. Noise and Vibration Control, 2022,42 (01): 41-47 [13] Deng L, Cai C S. Bridge Model Updating Using Response Surface Method and Genetic Algorithm[J]. Journal of Bridge Engineering, 2010, 15(5):P.553-564. [14] Do D M, Gao W, Song C, et al. Dynamic analysis and reliability assessment of structures with uncertain-but-bounded parameters under stochastic process excitations[J]. Reliability Engineering & System Safety, 2014, 132:46-59. [15] Nanda, Bharadwaj, Maity, et al. Crack Assessment in Frame Structures Using Modal Data and Unified Particle Swarm Optimization Technique[J]. Advances in Structural Engineering, 2014. [16] 徐喆,辛景舟,唐启智等.基于响应面法和麻雀搜索算法的结构有限元模型修正[J].科学技术与工程,2021,21(21):9094-9101. Xu Z, Xin J Z, Tang Q Z, et al. Structural finite element model modification based on response surface method and sparrow search algorithm [J]. Science and Technology and Engineering, 2021,21 (21): 9094-9101 [17] 唐煜,岳杰,华旭刚.基于人工蜂群算法的桥梁有限元模型局部刚度修正[J].铁道科学与工程学报,2021,18(09):2333-2343. Tang Y, Yue J, Hua X G. Local stiffness correction of bridge finite element model based on artificial bee colony algorithm [J]. Journal of Railway Science and Engineering, 2021,18 (09): 2333-2343 [18] 付雷,马闻达,高鹏等.基于RSM-WOA的桥梁结构有限元模型修正[J].中国科技论文,2021,16(08):875-882. Fu Lei, Ma W D, Gao P, et al. Finite Element Model Modification of Bridge Structures Based on RSM-WOA [J]. Chinese Science and Technology Paper, 2021,16 (08): 875-882. [19] Perera R , Fang S E , Ruiz A . Application of particle swarm optimization and genetic algorithms tomultiobjective damage identification inverse problems with modelling errors[J]. Meccanica, 2010, 45(5):723-734. [20] Shabbir F, Omenzetter P. Particle Swarm Optimization with Sequential Niche Technique for Dynamic Finite Element Model Updating[J]. Computer-Aided Civil and Infrastructure Engineering, 2015, 30(5):359-375. [21] Wan H P, Ren W X. Parameter Selection in Finite-Element-Model Updating by Global Sensitivity Analysis Using Gaussian Process Metamodel[J]. Journal of Structural Engineering, 2015, 141(6):04014164. [22] Fang S E, Ren W X, Perera R. A stochastic model updating method for parameter variability quantification based on response surface models and Monte Carlo simulation[J]. Mechanical Systems & Signal Processing, 2012, 33(NOV.):p.83-96. [23] JTG D60-2004, 公路桥涵设计通用规范[S]. JTG D60-2004, General Specification for Design of Highway Bridges and Culverts [S]. [24] JTG/T J20-01-2015，公路桥梁荷载试验规程[S]. JTG/T J20-01-2015, Load Test Specification for Highway Bridges [S] . [25] 虢曙安.特大跨径钢桁梁悬索桥静载试验[J].长安大学学报(自然科学版),2020,40(06):67-76. Guo S A. Static load test of super large span steel truss Suspension bridge [J]. Journal of Chang'an University (Natural Science Edition), 2020,40 (06): 67-76 [26] 宋晓东,颜永逸,李佳靖等.基于子结构的大型桥梁有限元模型修正方法[J].桥梁建设,2021,51(02):40-46. Song X D, Yan Y Y, Li J J, etc. Finite element model updating method for large bridges based on Substructure [J]. Bridge Construction, 2021,51 (02): 40-46 [27] 石颉,杜国庆.改进麻雀搜索算法优化SVM的方法及应用[J].计算机工程与设计,2023,44(03):954-961. Shi J, Du G Q. Method and Application of Improving Sparrow Search Algorithm to Optimize SVM [J]. Computer Engineering and Design, 2023,44 (03): 954-961 [28] Fathy A, Rezk H, Yousri D, et al. Real-time bald eagle search approach for tracking the maximum generated power of wind energy conversion system[J]. Energy, 2022, 249. [29] Zhang C, Ding S. A stochastic configuration network based on chaotic sparrow search algorithm[J]. Knowledge-Based Systems, 2021, 220(10):106924. [30] Singh M, Patra S K. Partial Transmit Sequence Optimization Using Improved Harmony Search Algorithm for PAPR Reduction in OFDM[J]. ETRI Journal, 2017, 39(6). [31] 段君邦,王华,郝天之等.基于改进灰狼算法和支持向量机的拱桥索力优化模型[J].计算机工程与设计,2023,44(02):457-465. Duan J B, Wang H, Hao T Z, et al. Optimization model for arch bridge cable tension based on improved Grey Wolf algorithm and support vector machine [J]. Computer Engineering and Design, 2023,44 (02): 457-465 [32] Tanyildizi E, Demir G. Golden Sine Algorithm: A Novel Math-Inspired Algorithm[J]. Advances in Electrical and Computer Engineering, 2017, 17(2):71-78. [33] 王克逸,符强,陈嘉豪.偏移进化蜉蝣优化算法[J].计算机系统应用,2022,31(03):150-158. Wang K Y, Fu Q, Chen J H. Offset Evolution Mayfly Optimization Algorithm [J]. Computer System Applications, 2022,31 (03): 150-158. [34] 杨文甫,陈鑫.基于最优径向基神经网络的大跨度悬索桥有限元模型修正[J].中外公路,2023,43(02):80-85. Yang W F, Chen X. Finite element model modification of long-span Suspension bridge based on optimal radial basis function neural network [J]. Zhongwai Highway, 2023,43 (02): 80-85. [35] 成魁,王祺顺,张祖军等.模拟退火算法与Python-ABAQUS结合的有限元模型修正方法研究[J].公路工程,2021,46(06):74-80. Cheng K, Wang Q S, Zhang Z J, et al. Research on Finite Element Model Correction Method Combining Simulated Annealing Algorithm with Python ABAQUS [J]. Highway Engineering, 2021,46 (06): 74-80 [36] 刘纲,陈奇,雷振博等.基于改进萤火虫算法的有限元模型修正[J].工程力学,2022,39(07):1-9. Liu G, Chen Q, Lei Z B, etc. Finite element model modification based on improved Firefly algorithm [J]. Applied mechanics, 2022,39 (07): 1-9. [37] 王文竹,李杰,刘刚等.基于Kriging代理模型鼓式制动器稳定性的优化设计[J].振动与冲击,2021,40(11):134-138+162. Wang W Z, Li J, Liu G, et al, Cheng Mianhong. Optimization Design of Drum Brake Stability Based on Kriging surrogate model [J]. Vibration and Shock, 2021,40 (11): 134-138+162.