Prediction for segment strain and opening of underwater shield tunnel using deep learning method
文献类型：期刊论文
| 作者 | Tan, Xuyan2,3; Chen, Weizhong2,3; Yang, Jianping2,3; Du, Bowen1; Zou, Tao1 |
| 刊名 | TRANSPORTATION GEOTECHNICS
 |
| 出版日期 | 2023-03-01 |
| 卷号 | 39期号:-页码:- |
| 关键词 | Tunnel Prediction Monitoring Deep learning Data analysis |
| ISSN号 | 2214-3912 |
| 英文摘要 | Predicting the future mechanical behaviors of tunnel structure is essential to prevent disasters and maintain the long-term stability. Due to the underwater shield tunnels are usually constructed in complicated geological conditions, it is a challenge for traditional models to accurately predict the future behaviors considering multiple influence factors comprehensively. In this study, a multi-learning model termed as GC-GRU was presented on the basis of deep learning algorithm to predict the future mechanical behaviors of tunnel structure, which was formalized on the structural health monitoring data obtained from the Wuhan Yangtze River tunnel. Based on GC-GRU, temporal dependencies of historical performance and the spatial correlations among different monitoring indicators were captured, and the segment strain and opening in next 45 days were predicted. In addition, a series of experiments were conducted to discuss the predictive capability of the presented model, including the comparison to single indictor prediction model and some widely used classical prediction models, such as GRU, LSTM, XGboost, LR, and RNN. The comparison results denoted that GC-GRU performed best among all models especially when the prediction time scale reaches 20 days. The predicted errors of GC-GRU deduce at least 0.02 mm and 8.62 mu epsilon for joint opening and segment strain respectively, and model learning capability improves 2.2% for both of them. Therefore, it is reliable to introduce GC-GRU model to predict the future mechanical behaviors of tunnel structure. |
| 学科主题 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000994888800001 |
| 出版者 | ELSEVIER |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/35220]  |
| 专题 | 中科院武汉岩土力学所 |
| 作者单位 | 1.SKLSDE Lab, Beihang University, Beijing 100191, China 2.State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China 3.University of Chinese Academy of Sciences, Beijing 100049, China |
| 推荐引用方式 GB/T 7714 | Tan, Xuyan,Chen, Weizhong,Yang, Jianping,et al. Prediction for segment strain and opening of underwater shield tunnel using deep learning method[J]. TRANSPORTATION GEOTECHNICS,2023,39(-):-. |
| APA | Tan, Xuyan,Chen, Weizhong,Yang, Jianping,Du, Bowen,&Zou, Tao.(2023).Prediction for segment strain and opening of underwater shield tunnel using deep learning method.TRANSPORTATION GEOTECHNICS,39(-),-. |
| MLA | Tan, Xuyan,et al."Prediction for segment strain and opening of underwater shield tunnel using deep learning method".TRANSPORTATION GEOTECHNICS 39.-(2023):-. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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