DATCN: Deep Attention fused Temporal Convolution Network for the prediction of monitoring indicators in the tunnel
文献类型：期刊论文
| 作者 | Du, Bowen; Zhang, Zhixin; Ye, Junchen; Tan, Xuyan; Li, Wentao; Chen, Weizhong |
| 刊名 | SMART STRUCTURES AND SYSTEMS
 |
| 出版日期 | 2022-12-01 |
| 卷号 | 30期号:6页码:601 |
| 关键词 | machine learning mechanical behaviors monitoring prediction tunnel |
| ISSN号 | 1738-1584 |
| 英文摘要 | The prediction of structural mechanical behaviors is vital important to early perceive the abnormal conditions and avoid the occurrence of disasters. Especially for underground engineering, complex geological conditions make the structure more prone to disasters. Aiming at solving the problems existing in previous studies, such as incomplete consideration factors and can only predict the continuous performance, the deep attention fused temporal convolution network (DATCN) is proposed in this paper to predict the spatial mechanical behaviors of structure, which integrates both the temporal effect and spatial effect and realize the cross-time prediction. The temporal convolution network (TCN) and self-attention mechanism are employed to learn the temporal correlation of each monitoring point and the spatial correlation among different points, respectively. Then, the predicted result obtained from DATCN is compared with that obtained from some classical baselines, including SVR, LR, MLP, and RNNs. Also, the parameters involved in DATCN are discussed to optimize the prediction ability. The prediction result demonstrates that the proposed DATCN model outperforms the state-of-the-art baselines. The prediction accuracy of DATCN model after 24 hours reaches 90 percent. Also, the performance in last 14 hours plays a domain role to predict the short-term behaviors of the structure. As a study case, the proposed model is applied in an underwater shield tunnel to predict the stress variation of concrete segments in space. |
| 学科主题 | Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:000914431400005 |
| 出版者 | TECHNO-PRESS |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/35202]  |
| 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 3.Beihang University; Beihang University; |
| 推荐引用方式 GB/T 7714 | Du, Bowen,Zhang, Zhixin,Ye, Junchen,et al. DATCN: Deep Attention fused Temporal Convolution Network for the prediction of monitoring indicators in the tunnel[J]. SMART STRUCTURES AND SYSTEMS,2022,30(6):601. |
| APA | Du, Bowen,Zhang, Zhixin,Ye, Junchen,Tan, Xuyan,Li, Wentao,&Chen, Weizhong.(2022).DATCN: Deep Attention fused Temporal Convolution Network for the prediction of monitoring indicators in the tunnel.SMART STRUCTURES AND SYSTEMS,30(6),601. |
| MLA | Du, Bowen,et al."DATCN: Deep Attention fused Temporal Convolution Network for the prediction of monitoring indicators in the tunnel".SMART STRUCTURES AND SYSTEMS 30.6(2022):601. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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