Temporal-spatial coupled model for multi-prediction of tunnel structure: using deep attention-based temporal convolutional network
文献类型：期刊论文
| 作者 | Tan, Xuyan; Chen, Weizhong; Yang, Jianping; Tan, Xianjun |
| 刊名 | JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING
 |
| 出版日期 | 2022-06-01 |
| 卷号 | 12期号:3页码:675 |
| 关键词 | Tunnel Deep learning Structural health monitoring Data analysis Prediction |
| ISSN号 | 2190-5452 |
| 英文摘要 | Predicting the mechanical behaviors of tunnel and subsurface facilities is an effective way to prevent accidental disasters. However, some drawbacks exist in many traditional prediction models, such as inadequate consideration of impacting factors, low predictive accuracy, and high computational cost. To this end, a coupled model based on deep attention-based temporal convolutional network (DATCN) is proposed for multiple prediction of structural mechanical behavior, where temporal convolutional network and self-attention mechanism are applied to learn temporal dependencies and spatial dependencies respectively. Subsequently, the DATCN model is formalized on a long-term dataset collected using a Structural Health Monitoring System in the Wuhan Yangtze River tunnel. Using three evaluation indicators, a series of data experiments are conducted to obtain the most appropriate parameters involved in the model and the superiority of DATCN over other commonly used models including LSTM, RNN, GRU, LR, and SVR is discussed. Experimental results indicate that future structural behavior shows a strong correlation between spatial dependencies and historical performance, especially that in the last 16 days. Moreover, the predictive capability of DATCN is the best compared to other commonly used models, whose predictive accuracy for the next 10 days is better than 88% and improved by 1.726% at least. Finally, the DATCN model is adopted to predict the structural behavior of the tunnel under extreme conditions as a field application, and the results suggest that the DATCN model is robust and accurate. |
| 学科主题 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000789002400001 |
| 出版者 | SPRINGER HEIDELBERG |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/34915]  |
| 专题 | 中科院武汉岩土力学所 |
| 作者单位 | 1.Chinese Academy of Sciences; Wuhan Institute of Rock & Soil Mechanics, CAS; 2.Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS |
| 推荐引用方式 GB/T 7714 | Tan, Xuyan,Chen, Weizhong,Yang, Jianping,et al. Temporal-spatial coupled model for multi-prediction of tunnel structure: using deep attention-based temporal convolutional network[J]. JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING,2022,12(3):675. |
| APA | Tan, Xuyan,Chen, Weizhong,Yang, Jianping,&Tan, Xianjun.(2022).Temporal-spatial coupled model for multi-prediction of tunnel structure: using deep attention-based temporal convolutional network.JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING,12(3),675. |
| MLA | Tan, Xuyan,et al."Temporal-spatial coupled model for multi-prediction of tunnel structure: using deep attention-based temporal convolutional network".JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING 12.3(2022):675. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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