The Performance Study on the Long-Span Bridge Involving the Wireless Sensor Network Technology in a Big Data Environment
The random traffic flow model which considers parameters of all the vehicles passing through the bridge, including arrival time, vehicle speed, vehicle type, vehicle weight, and horizontal position as well as the bridge deck roughness, is input into the vehicle-bridge coupling vibration program. In...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/4154673 |
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| _version_ | 1832556130547531776 |
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| author | Liwen Zhang Chao Zhang Zhuo Sun You Dong Pu Wei |
| author_facet | Liwen Zhang Chao Zhang Zhuo Sun You Dong Pu Wei |
| author_sort | Liwen Zhang |
| collection | DOAJ |
| description | The random traffic flow model which considers parameters of all the vehicles passing through the bridge, including arrival time, vehicle speed, vehicle type, vehicle weight, and horizontal position as well as the bridge deck roughness, is input into the vehicle-bridge coupling vibration program. In this way, vehicle-bridge coupling vibration responses with considering the random traffic flow can be numerically simulated. Experimental test is used to validate the numerical simulation, and they had the consistent changing trends. This result proves the reliability of the vehicle-bridge coupling model in this paper. However, the computational process of this method is complicated and proposes high requirements for computer performance and resources. Therefore, this paper considers using a more advanced intelligent method to predict vibration responses of the long-span bridge. The PSO-BP (particle swarm optimization-back propagation) neural network model is proposed to predict vibration responses of the long-span bridge. Predicted values and real values at each point basically have the consistent changing trends, and the maximum error is less than 10%. Hence, it is feasible to predict vibration responses of the long-span bridge using the PSO-BP neural network model. In order to verify advantages of the predicting model, it is compared with the BP neural network model and GA-BP neural network model. The PSO-BP neural network model converges to the set critical error after it is iterated to the 226th generation, while the other two neural network models are not converged. In addition, the relative error of predicted values using PSO-BP neural network is only 2.71%, which is obviously less than the predicted results of other two neural network models. We can find that the PSO-BP neural network model proposed by the paper in predicting vibration responses is highly efficient and accurate. |
| format | Article |
| id | doaj-art-ff29a9e39b684887aa826c1d93f74fe8 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-ff29a9e39b684887aa826c1d93f74fe82025-02-03T05:46:20ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/41546734154673The Performance Study on the Long-Span Bridge Involving the Wireless Sensor Network Technology in a Big Data EnvironmentLiwen Zhang0Chao Zhang1Zhuo Sun2You Dong3Pu Wei4Department of Civil Engineering, Guangzhou University, Guangzhou, ChinaDepartment of Civil Engineering, Guangzhou University, Guangzhou, ChinaDepartment of Civil Engineering, Guangzhou University, Guangzhou, ChinaDepartment of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong KongShanghai Municipal Engineering Design Institute, Shanghai, ChinaThe random traffic flow model which considers parameters of all the vehicles passing through the bridge, including arrival time, vehicle speed, vehicle type, vehicle weight, and horizontal position as well as the bridge deck roughness, is input into the vehicle-bridge coupling vibration program. In this way, vehicle-bridge coupling vibration responses with considering the random traffic flow can be numerically simulated. Experimental test is used to validate the numerical simulation, and they had the consistent changing trends. This result proves the reliability of the vehicle-bridge coupling model in this paper. However, the computational process of this method is complicated and proposes high requirements for computer performance and resources. Therefore, this paper considers using a more advanced intelligent method to predict vibration responses of the long-span bridge. The PSO-BP (particle swarm optimization-back propagation) neural network model is proposed to predict vibration responses of the long-span bridge. Predicted values and real values at each point basically have the consistent changing trends, and the maximum error is less than 10%. Hence, it is feasible to predict vibration responses of the long-span bridge using the PSO-BP neural network model. In order to verify advantages of the predicting model, it is compared with the BP neural network model and GA-BP neural network model. The PSO-BP neural network model converges to the set critical error after it is iterated to the 226th generation, while the other two neural network models are not converged. In addition, the relative error of predicted values using PSO-BP neural network is only 2.71%, which is obviously less than the predicted results of other two neural network models. We can find that the PSO-BP neural network model proposed by the paper in predicting vibration responses is highly efficient and accurate.http://dx.doi.org/10.1155/2018/4154673 |
| spellingShingle | Liwen Zhang Chao Zhang Zhuo Sun You Dong Pu Wei The Performance Study on the Long-Span Bridge Involving the Wireless Sensor Network Technology in a Big Data Environment Complexity |
| title | The Performance Study on the Long-Span Bridge Involving the Wireless Sensor Network Technology in a Big Data Environment |
| title_full | The Performance Study on the Long-Span Bridge Involving the Wireless Sensor Network Technology in a Big Data Environment |
| title_fullStr | The Performance Study on the Long-Span Bridge Involving the Wireless Sensor Network Technology in a Big Data Environment |
| title_full_unstemmed | The Performance Study on the Long-Span Bridge Involving the Wireless Sensor Network Technology in a Big Data Environment |
| title_short | The Performance Study on the Long-Span Bridge Involving the Wireless Sensor Network Technology in a Big Data Environment |
| title_sort | performance study on the long span bridge involving the wireless sensor network technology in a big data environment |
| url | http://dx.doi.org/10.1155/2018/4154673 |
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