Safety Prediction Using Vehicle Safety Evaluation Model Passing on Long-Span Bridge with Fully Connected Neural Network
The safety condition of vehicles passing on long-span bridges has attracted more and more attention in recent years. Many research studies have been done to find convenience and efficiency measures. A vehicle safety evaluation model passing on a long-span bridge is presented in this paper based on f...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2019/8130240 |
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| _version_ | 1850174497349435392 |
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| author | Yang Yang Lin Yang Bo Wu Gang Yao Hang Li Soltys Robert |
| author_facet | Yang Yang Lin Yang Bo Wu Gang Yao Hang Li Soltys Robert |
| author_sort | Yang Yang |
| collection | DOAJ |
| description | The safety condition of vehicles passing on long-span bridges has attracted more and more attention in recent years. Many research studies have been done to find convenience and efficiency measures. A vehicle safety evaluation model passing on a long-span bridge is presented in this paper based on fully connected neural network (FCN). The first step is to investigate the long-span bridge responses with wind excitation by using the wind tunnel test and finite element model. Subsequently, typical vehicle models are given and a vehicle-bridge system is established by considering weather conditions. Accident types of vehicles with severe weather are estimated. In particular, the input and output variables of the vehicle safety evaluation model are determined, and simultaneously training, validation, and testing data are achieved. Twenty-nine models have been compared and analyzed by using hidden layer, initial learning rate, batch size, activation function, and optimization method. It is found that the 4-15-15-4 model occupies a preferable prediction performance, and it can provide a kind of utility for traffic control and reduce the probability of vehicle accidents on the bridge. |
| format | Article |
| id | doaj-art-d5f153c92a4b4a5d9152bab3ea665834 |
| institution | OA Journals |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-d5f153c92a4b4a5d9152bab3ea6658342025-08-20T02:19:38ZengWileyAdvances in Civil Engineering1687-80861687-80942019-01-01201910.1155/2019/81302408130240Safety Prediction Using Vehicle Safety Evaluation Model Passing on Long-Span Bridge with Fully Connected Neural NetworkYang Yang0Lin Yang1Bo Wu2Gang Yao3Hang Li4Soltys Robert5Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing, ChinaKey Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing, ChinaKey Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing, ChinaKey Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing, ChinaKey Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing, ChinaTechnical University of Kosice, Kosice, SlovakiaThe safety condition of vehicles passing on long-span bridges has attracted more and more attention in recent years. Many research studies have been done to find convenience and efficiency measures. A vehicle safety evaluation model passing on a long-span bridge is presented in this paper based on fully connected neural network (FCN). The first step is to investigate the long-span bridge responses with wind excitation by using the wind tunnel test and finite element model. Subsequently, typical vehicle models are given and a vehicle-bridge system is established by considering weather conditions. Accident types of vehicles with severe weather are estimated. In particular, the input and output variables of the vehicle safety evaluation model are determined, and simultaneously training, validation, and testing data are achieved. Twenty-nine models have been compared and analyzed by using hidden layer, initial learning rate, batch size, activation function, and optimization method. It is found that the 4-15-15-4 model occupies a preferable prediction performance, and it can provide a kind of utility for traffic control and reduce the probability of vehicle accidents on the bridge.http://dx.doi.org/10.1155/2019/8130240 |
| spellingShingle | Yang Yang Lin Yang Bo Wu Gang Yao Hang Li Soltys Robert Safety Prediction Using Vehicle Safety Evaluation Model Passing on Long-Span Bridge with Fully Connected Neural Network Advances in Civil Engineering |
| title | Safety Prediction Using Vehicle Safety Evaluation Model Passing on Long-Span Bridge with Fully Connected Neural Network |
| title_full | Safety Prediction Using Vehicle Safety Evaluation Model Passing on Long-Span Bridge with Fully Connected Neural Network |
| title_fullStr | Safety Prediction Using Vehicle Safety Evaluation Model Passing on Long-Span Bridge with Fully Connected Neural Network |
| title_full_unstemmed | Safety Prediction Using Vehicle Safety Evaluation Model Passing on Long-Span Bridge with Fully Connected Neural Network |
| title_short | Safety Prediction Using Vehicle Safety Evaluation Model Passing on Long-Span Bridge with Fully Connected Neural Network |
| title_sort | safety prediction using vehicle safety evaluation model passing on long span bridge with fully connected neural network |
| url | http://dx.doi.org/10.1155/2019/8130240 |
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