ALSTNet: Autoencoder fused long‐ and short‐term time‐series network for the prediction of tunnel structure
Abstract It is crucial to predict future mechanical behaviors for the prevention of structural disasters. Especially for underground construction, the structural mechanical behaviors are affected by multiple internal and external factors due to the complex conditions. Given that the existing models...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-03-01
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| Series: | Deep Underground Science and Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/dug2.12081 |
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| _version_ | 1850053835033149440 |
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| author | Bowen Du Haohan Liang Yuhang Wang Junchen Ye Xuyan Tan Weizhong Chen |
| author_facet | Bowen Du Haohan Liang Yuhang Wang Junchen Ye Xuyan Tan Weizhong Chen |
| author_sort | Bowen Du |
| collection | DOAJ |
| description | Abstract It is crucial to predict future mechanical behaviors for the prevention of structural disasters. Especially for underground construction, the structural mechanical behaviors are affected by multiple internal and external factors due to the complex conditions. Given that the existing models fail to take into account all the factors and accurate prediction of the multiple time series simultaneously is difficult using these models, this study proposed an improved prediction model through the autoencoder fused long‐ and short‐term time‐series network driven by the mass number of monitoring data. Then, the proposed model was formalized on multiple time series of strain monitoring data. Also, the discussion analysis with a classical baseline and an ablation experiment was conducted to verify the effectiveness of the prediction model. As the results indicate, the proposed model shows obvious superiority in predicting the future mechanical behaviors of structures. As a case study, the presented model was applied to the Nanjing Dinghuaimen tunnel to predict the stain variation on a different time scale in the future. |
| format | Article |
| id | doaj-art-74bdc06cfec2403382b939a5ace7d529 |
| institution | DOAJ |
| issn | 2097-0668 2770-1328 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Deep Underground Science and Engineering |
| spelling | doaj-art-74bdc06cfec2403382b939a5ace7d5292025-08-20T02:52:26ZengWileyDeep Underground Science and Engineering2097-06682770-13282025-03-0141728210.1002/dug2.12081ALSTNet: Autoencoder fused long‐ and short‐term time‐series network for the prediction of tunnel structureBowen Du0Haohan Liang1Yuhang Wang2Junchen Ye3Xuyan Tan4Weizhong Chen5State Key Laboratory of Software Development Environment Beihang University Beijing ChinaState Key Laboratory of Software Development Environment Beihang University Beijing ChinaState Key Laboratory of Software Development Environment Beihang University Beijing ChinaState Key Laboratory of Software Development Environment Beihang University Beijing ChinaState Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics Chinese Academy of Sciences Wuhan ChinaState Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics Chinese Academy of Sciences Wuhan ChinaAbstract It is crucial to predict future mechanical behaviors for the prevention of structural disasters. Especially for underground construction, the structural mechanical behaviors are affected by multiple internal and external factors due to the complex conditions. Given that the existing models fail to take into account all the factors and accurate prediction of the multiple time series simultaneously is difficult using these models, this study proposed an improved prediction model through the autoencoder fused long‐ and short‐term time‐series network driven by the mass number of monitoring data. Then, the proposed model was formalized on multiple time series of strain monitoring data. Also, the discussion analysis with a classical baseline and an ablation experiment was conducted to verify the effectiveness of the prediction model. As the results indicate, the proposed model shows obvious superiority in predicting the future mechanical behaviors of structures. As a case study, the presented model was applied to the Nanjing Dinghuaimen tunnel to predict the stain variation on a different time scale in the future.https://doi.org/10.1002/dug2.12081autoencoderdeep learningstructural health monitoringtime‐series prediction |
| spellingShingle | Bowen Du Haohan Liang Yuhang Wang Junchen Ye Xuyan Tan Weizhong Chen ALSTNet: Autoencoder fused long‐ and short‐term time‐series network for the prediction of tunnel structure Deep Underground Science and Engineering autoencoder deep learning structural health monitoring time‐series prediction |
| title | ALSTNet: Autoencoder fused long‐ and short‐term time‐series network for the prediction of tunnel structure |
| title_full | ALSTNet: Autoencoder fused long‐ and short‐term time‐series network for the prediction of tunnel structure |
| title_fullStr | ALSTNet: Autoencoder fused long‐ and short‐term time‐series network for the prediction of tunnel structure |
| title_full_unstemmed | ALSTNet: Autoencoder fused long‐ and short‐term time‐series network for the prediction of tunnel structure |
| title_short | ALSTNet: Autoencoder fused long‐ and short‐term time‐series network for the prediction of tunnel structure |
| title_sort | alstnet autoencoder fused long and short term time series network for the prediction of tunnel structure |
| topic | autoencoder deep learning structural health monitoring time‐series prediction |
| url | https://doi.org/10.1002/dug2.12081 |
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