Nonlinear modeling of temperature-induced bearing displacement of long-span single-pier rigid frame bridge based on DCNN-LSTM
Long-span single-pier rigid frame bridge may experience excessive bearing displacement under temperature variation, which can result in structural deformation or instability, causing significant engineering accidents and immeasurable losses on society. This paper proposes a spatial-temporal nonlinea...
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| Published in | Case studies in thermal engineering Vol. 53; p. 103897 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier
01.01.2024
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| Abstract | Long-span single-pier rigid frame bridge may experience excessive bearing displacement under temperature variation, which can result in structural deformation or instability, causing significant engineering accidents and immeasurable losses on society. This paper proposes a spatial-temporal nonlinear modeling method for temperature and temperature-induced bearing displacement (TIBD) of long-span single-pier rigid frame bridge based on DCNN-LSTM network with elastic modulus fusion, relying on the monitoring data of the Second Yangtze River Bridge in Wuhan. This framework introduces a dynamic autoregressive method for model inference to improve the prediction accuracy of the model. This study addresses three major issues: Insufficient rational selection of temperature characteristic values; Inadequate research on correlation between structural temperature and TIBD for the bridges; Poor prediction accuracy of temperature-TIBD regression models. The method proposed in this study achieves prediction accuracy of up to 99.8 % for TIBD, and this model is not affected by seasonal variation. |
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| AbstractList | Long-span single-pier rigid frame bridge may experience excessive bearing displacement under temperature variation, which can result in structural deformation or instability, causing significant engineering accidents and immeasurable losses on society. This paper proposes a spatial-temporal nonlinear modeling method for temperature and temperature-induced bearing displacement (TIBD) of long-span single-pier rigid frame bridge based on DCNN-LSTM network with elastic modulus fusion, relying on the monitoring data of the Second Yangtze River Bridge in Wuhan. This framework introduces a dynamic autoregressive method for model inference to improve the prediction accuracy of the model. This study addresses three major issues: Insufficient rational selection of temperature characteristic values; Inadequate research on correlation between structural temperature and TIBD for the bridges; Poor prediction accuracy of temperature-TIBD regression models. The method proposed in this study achieves prediction accuracy of up to 99.8 % for TIBD, and this model is not affected by seasonal variation. |
| ArticleNumber | 103897 |
| Author | Deng, Zhihang Hu, Junliang Ye, Zhongtao Huang, Minshui Wan, Neng Zhang, Jianwei |
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| Snippet | Long-span single-pier rigid frame bridge may experience excessive bearing displacement under temperature variation, which can result in structural deformation... |
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| SubjectTerms | Autoregression Deep convolutional neural network Long short-term memory neural network Long-span single-pier rigid frame bridge Structural health monitoring Temperature-induced bearing displacement |
| Title | Nonlinear modeling of temperature-induced bearing displacement of long-span single-pier rigid frame bridge based on DCNN-LSTM |
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