Application of a Bi-Mamba Model for Railway Subgrade Settlement Prediction During Pipe-Jacking Tunneling
To explore a more accurate prediction method for subgrade settlement induced by underpass construction, this study takes the existing railway project of Ningbo Yuanyi Road underpass as a case to construct a subgrade settlement prediction model based on the Mamba neural network. Using monitoring data...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/14/7790 |
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| author | Yipu Peng Ning Zhou Bin Wang Hongjun Gan |
| author_facet | Yipu Peng Ning Zhou Bin Wang Hongjun Gan |
| author_sort | Yipu Peng |
| collection | DOAJ |
| description | To explore a more accurate prediction method for subgrade settlement induced by underpass construction, this study takes the existing railway project of Ningbo Yuanyi Road underpass as a case to construct a subgrade settlement prediction model based on the Mamba neural network. Using monitoring data collected using on-site automated monitoring robots as the data foundation, the prediction results of the improved transformer, long short-term memory (LSTM), time-series dense encoder (Tide), and decomposition-linear (Dlinear) neural networks are compared. The research results show that the Mean Squared Error (MSE) and Mean Absolute Error (MAE) of the proposed Bi-Mamba model are 0.279 and 0.276, respectively, demonstrating higher prediction accuracy than comparative models such as iTransformer and LSTM. Additionally, ablation experiments verify that the attention gating module in the model reduces the MSE by 9.1%, serving as a key component for improving accuracy. This study provides an advanced data-driven prediction method for subgrade settlement forecasting, offering technical references for similar engineering projects. |
| format | Article |
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| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-fd642064c39b40fdabe6ce864fdf43a42025-08-20T03:58:25ZengMDPI AGApplied Sciences2076-34172025-07-011514779010.3390/app15147790Application of a Bi-Mamba Model for Railway Subgrade Settlement Prediction During Pipe-Jacking TunnelingYipu Peng0Ning Zhou1Bin Wang2Hongjun Gan3School of Civil Engineering, Central South University, Changsha 410075, ChinaSchool of Civil Engineering, Central South University, Changsha 410075, ChinaSchool of Civil Engineering, Central South University, Changsha 410075, ChinaSchool of Civil Engineering, Central South University, Changsha 410075, ChinaTo explore a more accurate prediction method for subgrade settlement induced by underpass construction, this study takes the existing railway project of Ningbo Yuanyi Road underpass as a case to construct a subgrade settlement prediction model based on the Mamba neural network. Using monitoring data collected using on-site automated monitoring robots as the data foundation, the prediction results of the improved transformer, long short-term memory (LSTM), time-series dense encoder (Tide), and decomposition-linear (Dlinear) neural networks are compared. The research results show that the Mean Squared Error (MSE) and Mean Absolute Error (MAE) of the proposed Bi-Mamba model are 0.279 and 0.276, respectively, demonstrating higher prediction accuracy than comparative models such as iTransformer and LSTM. Additionally, ablation experiments verify that the attention gating module in the model reduces the MSE by 9.1%, serving as a key component for improving accuracy. This study provides an advanced data-driven prediction method for subgrade settlement forecasting, offering technical references for similar engineering projects.https://www.mdpi.com/2076-3417/15/14/7790Mambadeep learningrailway engineeringsubgrade settlement |
| spellingShingle | Yipu Peng Ning Zhou Bin Wang Hongjun Gan Application of a Bi-Mamba Model for Railway Subgrade Settlement Prediction During Pipe-Jacking Tunneling Applied Sciences Mamba deep learning railway engineering subgrade settlement |
| title | Application of a Bi-Mamba Model for Railway Subgrade Settlement Prediction During Pipe-Jacking Tunneling |
| title_full | Application of a Bi-Mamba Model for Railway Subgrade Settlement Prediction During Pipe-Jacking Tunneling |
| title_fullStr | Application of a Bi-Mamba Model for Railway Subgrade Settlement Prediction During Pipe-Jacking Tunneling |
| title_full_unstemmed | Application of a Bi-Mamba Model for Railway Subgrade Settlement Prediction During Pipe-Jacking Tunneling |
| title_short | Application of a Bi-Mamba Model for Railway Subgrade Settlement Prediction During Pipe-Jacking Tunneling |
| title_sort | application of a bi mamba model for railway subgrade settlement prediction during pipe jacking tunneling |
| topic | Mamba deep learning railway engineering subgrade settlement |
| url | https://www.mdpi.com/2076-3417/15/14/7790 |
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