Prediction for the future mechanical behavior of underwater shield tunnel fusing deep learning algorithm on SHM data
文献类型：期刊论文
| 作者 | Tan, Xuyan2,3; Chen, Weizhong2,3; Tan, Xianjun2,3; Zou, Tao1; Du, Bowen1 |
| 刊名 | TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
 |
| 出版日期 | 2022-07-01 |
| 卷号 | 125期号:-页码:- |
| 关键词 | Tunnel Deep learning Structural health monitoring Prediction Strain Deep learning |
| ISSN号 | 0886-7798 |
| 英文摘要 | Predicting the future mechanical behavior of tunnel structure is vitally important to prevent accident disasters. However, in most of the existing models, the inadequate consideration for influencing factors reduced the final prediction accuracy. To this end, this study aims to develop an accurate prediction model considering the coupling effects of multiple influencing factors. First, the framework of model integrates the effects of Temporal , Spatial, and Load (TSL) dependencies is developed based on deep learning algorithm. Subsequently, TSL is formulated on the monitoring data obtained from the Wuhan Yangtze River tunnel and used to predict the mechanical behavior of this study case under an extreme condition. Through a series of experiments, the ne-cessity of considering the coupling effects of multiple influencing factors is verified, and the parameter effects on model predictive capability are discussed. In addition, some commonly used prediction models, such as RNN, LSTM, Xgboost, SVR, LR, are selected as baselines to compare with TSL. Experimental results indicate that the predictive ability of TSL is superior among al l models, whose accuracy improves 2.853% in next 15 days pre-diction. Therefore, it is essential to consider the couple effects of multiple factors, and the presented model is reasonable. |
| 学科主题 | Construction & Building Technology ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000797261700001 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/35143]  |
| 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,Tan, Xianjun,et al. Prediction for the future mechanical behavior of underwater shield tunnel fusing deep learning algorithm on SHM data[J]. TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY,2022,125(-):-. |
| APA | Tan, Xuyan,Chen, Weizhong,Tan, Xianjun,Zou, Tao,&Du, Bowen.(2022).Prediction for the future mechanical behavior of underwater shield tunnel fusing deep learning algorithm on SHM data.TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY,125(-),-. |
| MLA | Tan, Xuyan,et al."Prediction for the future mechanical behavior of underwater shield tunnel fusing deep learning algorithm on SHM data".TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 125.-(2022):-. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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