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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/atr/7189559 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 2042-3195 |