Dynamic Coupling Analysis of Vehicle-Bridge System for Long-Span Suspension Bridge Based on Backpropagation Neural Network Method

As the suspension bridge structures become more flexible and the forms of the vehicle load become more diverse, the dynamic coupling problem of the vehicle-bridge system has become gradually prominent in long-span suspension bridges, resulting in an increase in accuracy and efficiency requirements f...

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Main Authors: Zuolong Luo, Xiaobo Zheng, Haoyun Yuan, Xirong Niu
Format: Article
Language:English
Published: Hindawi Limited 2020-01-01
Series:Advances in Civil Engineering
Online Access:http://dx.doi.org/10.1155/2020/5878426
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spelling doaj-8c65bf14593a4a979e518926ab938d912020-11-25T02:45:15ZengHindawi LimitedAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/58784265878426Dynamic Coupling Analysis of Vehicle-Bridge System for Long-Span Suspension Bridge Based on Backpropagation Neural Network MethodZuolong Luo0Xiaobo Zheng1Haoyun Yuan2Xirong Niu3Department of Civil Engineering, Shanxi University, Taiyuan 030000, ChinaKey Laboratory of Transport Industry of Bridge Detection Reinforcement Technology, Chang’an University, Xi’an 710064, ChinaDepartment of Bridge Engineering, Chang’an University, Xi’an 710064, ChinaDepartment of Civil Engineering, Shanxi University, Taiyuan 030000, ChinaAs the suspension bridge structures become more flexible and the forms of the vehicle load become more diverse, the dynamic coupling problem of the vehicle-bridge system has become gradually prominent in long-span suspension bridges, resulting in an increase in accuracy and efficiency requirements for dynamic coupling analysis of the vehicle-bridge system. Conventional method such as finite element method (FEM) for dynamic coupling analysis of vehicle-bridge system often requires separate iteration of vehicle system and bridge system, and the contact and coupling interactions between them are used as the link for convergence inspection, which is too computationally intensive and time-consuming. In addition, the dynamic response of the vehicle-bridge coupling system obtained by FEM cannot be expressed explicitly, which is not convenient for engineering application. To overcome these drawbacks mentioned above, the backpropagation (BP) neural network technology is proposed to the dynamic coupling analysis of the vehicle-bridge system of long-span suspension bridges. Firstly, the BP neural network was used to approximate the dynamic response of the suspension bridge in the vehicle-bridge coupling system, and the complex finite element analysis results were thus explicitly displayed in the form of a mathematical analytical expression. And then the dynamic response of the suspension bridge under vehicle load was obtained by using a dynamic explicit analysis method. It is shown through a numerical example that, compared with FEM, the proposed method is much more economical to achieve reasonable accuracy when dealing with the dynamic coupling problem of the vehicle-bridge system. Finally, an engineering case involving a detailed finite element model of a long-span suspension bridge with a main span of 1688 m is presented to demonstrate the applicability and efficiency under the premise of ensuring the approximation accuracy, which indicates that the proposed method provides a new approach for dynamic coupling analysis of the vehicle-bridge system of long-span suspension bridges.http://dx.doi.org/10.1155/2020/5878426
collection DOAJ
language English
format Article
sources DOAJ
author Zuolong Luo
Xiaobo Zheng
Haoyun Yuan
Xirong Niu
spellingShingle Zuolong Luo
Xiaobo Zheng
Haoyun Yuan
Xirong Niu
Dynamic Coupling Analysis of Vehicle-Bridge System for Long-Span Suspension Bridge Based on Backpropagation Neural Network Method
Advances in Civil Engineering
author_facet Zuolong Luo
Xiaobo Zheng
Haoyun Yuan
Xirong Niu
author_sort Zuolong Luo
title Dynamic Coupling Analysis of Vehicle-Bridge System for Long-Span Suspension Bridge Based on Backpropagation Neural Network Method
title_short Dynamic Coupling Analysis of Vehicle-Bridge System for Long-Span Suspension Bridge Based on Backpropagation Neural Network Method
title_full Dynamic Coupling Analysis of Vehicle-Bridge System for Long-Span Suspension Bridge Based on Backpropagation Neural Network Method
title_fullStr Dynamic Coupling Analysis of Vehicle-Bridge System for Long-Span Suspension Bridge Based on Backpropagation Neural Network Method
title_full_unstemmed Dynamic Coupling Analysis of Vehicle-Bridge System for Long-Span Suspension Bridge Based on Backpropagation Neural Network Method
title_sort dynamic coupling analysis of vehicle-bridge system for long-span suspension bridge based on backpropagation neural network method
publisher Hindawi Limited
series Advances in Civil Engineering
issn 1687-8086
1687-8094
publishDate 2020-01-01
description As the suspension bridge structures become more flexible and the forms of the vehicle load become more diverse, the dynamic coupling problem of the vehicle-bridge system has become gradually prominent in long-span suspension bridges, resulting in an increase in accuracy and efficiency requirements for dynamic coupling analysis of the vehicle-bridge system. Conventional method such as finite element method (FEM) for dynamic coupling analysis of vehicle-bridge system often requires separate iteration of vehicle system and bridge system, and the contact and coupling interactions between them are used as the link for convergence inspection, which is too computationally intensive and time-consuming. In addition, the dynamic response of the vehicle-bridge coupling system obtained by FEM cannot be expressed explicitly, which is not convenient for engineering application. To overcome these drawbacks mentioned above, the backpropagation (BP) neural network technology is proposed to the dynamic coupling analysis of the vehicle-bridge system of long-span suspension bridges. Firstly, the BP neural network was used to approximate the dynamic response of the suspension bridge in the vehicle-bridge coupling system, and the complex finite element analysis results were thus explicitly displayed in the form of a mathematical analytical expression. And then the dynamic response of the suspension bridge under vehicle load was obtained by using a dynamic explicit analysis method. It is shown through a numerical example that, compared with FEM, the proposed method is much more economical to achieve reasonable accuracy when dealing with the dynamic coupling problem of the vehicle-bridge system. Finally, an engineering case involving a detailed finite element model of a long-span suspension bridge with a main span of 1688 m is presented to demonstrate the applicability and efficiency under the premise of ensuring the approximation accuracy, which indicates that the proposed method provides a new approach for dynamic coupling analysis of the vehicle-bridge system of long-span suspension bridges.
url http://dx.doi.org/10.1155/2020/5878426
work_keys_str_mv AT zuolongluo dynamiccouplinganalysisofvehiclebridgesystemforlongspansuspensionbridgebasedonbackpropagationneuralnetworkmethod
AT xiaobozheng dynamiccouplinganalysisofvehiclebridgesystemforlongspansuspensionbridgebasedonbackpropagationneuralnetworkmethod
AT haoyunyuan dynamiccouplinganalysisofvehiclebridgesystemforlongspansuspensionbridgebasedonbackpropagationneuralnetworkmethod
AT xirongniu dynamiccouplinganalysisofvehiclebridgesystemforlongspansuspensionbridgebasedonbackpropagationneuralnetworkmethod
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