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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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/5878426 |
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| author | Zuolong Luo Xiaobo Zheng Haoyun Yuan Xirong Niu |
| author_facet | Zuolong Luo Xiaobo Zheng Haoyun Yuan Xirong Niu |
| author_sort | Zuolong Luo |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-8c65bf14593a4a979e518926ab938d91 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-8c65bf14593a4a979e518926ab938d912025-02-03T06:46:56ZengWileyAdvances 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 |
| 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 |
| title | 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_short | 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 |
| 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 |