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A platform for research: civil engineering, architecture and urbanism
ALSTNet: Autoencoder fused long‐ and short‐term time‐series network for the prediction of tunnel structure
https://orcid.org/0009-0003-8306-6346
https://orcid.org/0000-0002-4919-7241
AbstractIt 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.
Highlights A novel data‐driven model, named autoencoder fused long‐ and short‐term time‐series network (ALSTNet), was presented for predicting the structural mechanical behaviors at multiple positions in the field. ALSTNet incorporated the impact of both long‐ and short‐term historical behaviors, as well as the spatial mechanical correlation achieved through encoding the networking that was formed by multivariate time series. Based on the proposed model, data experiments were conducted using monitoring data recorded by a structural health monitoring system installed in an underwater shield tunnel. The predicted results obtained from ALSTNet were compared with those from several baseline models, including linear regression, support vector regression, multilayer perceptron, long short‐term memory, and recurrent neural network. The findings reveal that ALSTNet outperforms the baseline models in terms of prediction accuracy, highlighting its effectiveness and superiority. As a crucial real‐world application, the presented model was used to predict the strain variation at multiple points in the Nanjing Dinghuaimen tunnel for 24 h on end. This application holds immense significance in preventing disasters in practical engineering scenarios and serves as an invaluable reference for similar engineering projects.
ALSTNet: Autoencoder fused long‐ and short‐term time‐series network for the prediction of tunnel structure
https://orcid.org/0009-0003-8306-6346
https://orcid.org/0000-0002-4919-7241
AbstractIt 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.
Highlights A novel data‐driven model, named autoencoder fused long‐ and short‐term time‐series network (ALSTNet), was presented for predicting the structural mechanical behaviors at multiple positions in the field. ALSTNet incorporated the impact of both long‐ and short‐term historical behaviors, as well as the spatial mechanical correlation achieved through encoding the networking that was formed by multivariate time series. Based on the proposed model, data experiments were conducted using monitoring data recorded by a structural health monitoring system installed in an underwater shield tunnel. The predicted results obtained from ALSTNet were compared with those from several baseline models, including linear regression, support vector regression, multilayer perceptron, long short‐term memory, and recurrent neural network. The findings reveal that ALSTNet outperforms the baseline models in terms of prediction accuracy, highlighting its effectiveness and superiority. As a crucial real‐world application, the presented model was used to predict the strain variation at multiple points in the Nanjing Dinghuaimen tunnel for 24 h on end. This application holds immense significance in preventing disasters in practical engineering scenarios and serves as an invaluable reference for similar engineering projects.
ALSTNet: Autoencoder fused long‐ and short‐term time‐series network for the prediction of tunnel structure
https://orcid.org/0009-0003-8306-6346
https://orcid.org/0000-0002-4919-7241
AbstractIt 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.
Highlights A novel data‐driven model, named autoencoder fused long‐ and short‐term time‐series network (ALSTNet), was presented for predicting the structural mechanical behaviors at multiple positions in the field. ALSTNet incorporated the impact of both long‐ and short‐term historical behaviors, as well as the spatial mechanical correlation achieved through encoding the networking that was formed by multivariate time series. Based on the proposed model, data experiments were conducted using monitoring data recorded by a structural health monitoring system installed in an underwater shield tunnel. The predicted results obtained from ALSTNet were compared with those from several baseline models, including linear regression, support vector regression, multilayer perceptron, long short‐term memory, and recurrent neural network. The findings reveal that ALSTNet outperforms the baseline models in terms of prediction accuracy, highlighting its effectiveness and superiority. As a crucial real‐world application, the presented model was used to predict the strain variation at multiple points in the Nanjing Dinghuaimen tunnel for 24 h on end. This application holds immense significance in preventing disasters in practical engineering scenarios and serves as an invaluable reference for similar engineering projects.
ALSTNet: Autoencoder fused long‐ and short‐term time‐series network for the prediction of tunnel structure
Deep Underground Science and Engineering
Du, Bowen (author) / Liang, Haohan (author) / Wang, Yuhang (author) / Ye, Junchen (author) / Tan, Xuyan (author) / Chen, Weizhong (author)
Deep Underground Science and Engineering ; 4 ; 72-82
2025-03-01
Article (Journal)
Electronic Resource
English
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