A Hybrid Spatial–Temporal Deep Learning Method for Metro Tunnel Displacement Prediction Under “Dual Carbon” Background
The burgeoning urbanization and construction activities pose significant challenges to the structural integrity and safety of the existing metro tunnels. This study introduces a hybrid spatial–temporal deep learning model, integrating graph convolutional network (GCN) and long short-term memory (LST...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/atr/7189559 |
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| author | Jianyong Chai Limin Jia Jianfeng Liu Enguang Hou Zhe Chen |
| author_facet | Jianyong Chai Limin Jia Jianfeng Liu Enguang Hou Zhe Chen |
| author_sort | Jianyong Chai |
| collection | DOAJ |
| description | The burgeoning urbanization and construction activities pose significant challenges to the structural integrity and safety of the existing metro tunnels. This study introduces a hybrid spatial–temporal deep learning model, integrating graph convolutional network (GCN) and long short-term memory (LSTM) networks, to predict metro tunnel displacements under the imperatives of “dual carbon” goals. The model leverages the strengths of GCNs in capturing spatial correlations and LSTM networks in processing temporal dynamics, offering a robust framework for accurate displacement prediction. The methodology encompasses data preprocessing, including outlier removal and missing value imputation, followed by feature extraction and normalization. The proposed GCN-LSTM model is trained on historical displacement data, employing a robotic total station (RTS) for high-precision monitoring. The model’s performance is evaluated using metrics such as root mean square error (RMSE), mean absolute error (MAE), and weighted mean absolute percentage error (WMAPE) and is compared against other models including LSTM, recurrent neural network (RNN), gated recurrent unit (GRU), residual LSTM (ResLSTM), and a variant of GCN-LSTM. The results indicate that the GCN-LSTM model outperforms comparative models across various sliding window sizes, demonstrating lower error metrics and higher stability. The model’s efficacy is further corroborated through a case study on the Jinan Metro Line 2, where it provides reliable predictions crucial for proactive maintenance and sustainable urban development. The study contributes to the field of metro tunnel displacement prediction and supports the advancement of intelligent monitoring systems for urban infrastructure. |
| format | Article |
| id | doaj-art-df5ce88321e940339711a6a0133dcffc |
| institution | OA Journals |
| issn | 2042-3195 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-df5ce88321e940339711a6a0133dcffc2025-08-20T02:24:14ZengWileyJournal of Advanced Transportation2042-31952025-01-01202510.1155/atr/7189559A Hybrid Spatial–Temporal Deep Learning Method for Metro Tunnel Displacement Prediction Under “Dual Carbon” BackgroundJianyong Chai0Limin Jia1Jianfeng Liu2Enguang Hou3Zhe Chen4School of Traffic and TransportationSchool of Traffic and TransportationDesign Research InstituteSchool of Rail TransportationSchool of Rail TransportationThe burgeoning urbanization and construction activities pose significant challenges to the structural integrity and safety of the existing metro tunnels. This study introduces a hybrid spatial–temporal deep learning model, integrating graph convolutional network (GCN) and long short-term memory (LSTM) networks, to predict metro tunnel displacements under the imperatives of “dual carbon” goals. The model leverages the strengths of GCNs in capturing spatial correlations and LSTM networks in processing temporal dynamics, offering a robust framework for accurate displacement prediction. The methodology encompasses data preprocessing, including outlier removal and missing value imputation, followed by feature extraction and normalization. The proposed GCN-LSTM model is trained on historical displacement data, employing a robotic total station (RTS) for high-precision monitoring. The model’s performance is evaluated using metrics such as root mean square error (RMSE), mean absolute error (MAE), and weighted mean absolute percentage error (WMAPE) and is compared against other models including LSTM, recurrent neural network (RNN), gated recurrent unit (GRU), residual LSTM (ResLSTM), and a variant of GCN-LSTM. The results indicate that the GCN-LSTM model outperforms comparative models across various sliding window sizes, demonstrating lower error metrics and higher stability. The model’s efficacy is further corroborated through a case study on the Jinan Metro Line 2, where it provides reliable predictions crucial for proactive maintenance and sustainable urban development. The study contributes to the field of metro tunnel displacement prediction and supports the advancement of intelligent monitoring systems for urban infrastructure.http://dx.doi.org/10.1155/atr/7189559 |
| spellingShingle | Jianyong Chai Limin Jia Jianfeng Liu Enguang Hou Zhe Chen A Hybrid Spatial–Temporal Deep Learning Method for Metro Tunnel Displacement Prediction Under “Dual Carbon” Background Journal of Advanced Transportation |
| title | A Hybrid Spatial–Temporal Deep Learning Method for Metro Tunnel Displacement Prediction Under “Dual Carbon” Background |
| title_full | A Hybrid Spatial–Temporal Deep Learning Method for Metro Tunnel Displacement Prediction Under “Dual Carbon” Background |
| title_fullStr | A Hybrid Spatial–Temporal Deep Learning Method for Metro Tunnel Displacement Prediction Under “Dual Carbon” Background |
| title_full_unstemmed | A Hybrid Spatial–Temporal Deep Learning Method for Metro Tunnel Displacement Prediction Under “Dual Carbon” Background |
| title_short | A Hybrid Spatial–Temporal Deep Learning Method for Metro Tunnel Displacement Prediction Under “Dual Carbon” Background |
| title_sort | hybrid spatial temporal deep learning method for metro tunnel displacement prediction under dual carbon background |
| url | http://dx.doi.org/10.1155/atr/7189559 |
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