A novel autoencoder for structural anomalies detection in river tunnel operation
文献类型：期刊论文
| 作者 | Tan, Xu-Yan1,2; Palaiahnakote, Shivakumara3; Chen, Weizhong1,2; Cheng, Ke4; Du, Bowen4 |
| 刊名 | EXPERT SYSTEMS WITH APPLICATIONS
 |
| 出版日期 | 2024-06-15 |
| 卷号 | 244页码:14 |
| 关键词 | Tunnel Machine learning Anomaly Autoencoder Extreme conditions Gaussian noise |
| ISSN号 | 0957-4174 |
| DOI | 10.1016/j.eswa.2023.122906 |
| 英文摘要 | Anomaly diagnosis is essential to prevent disasters and ensure long-term stable operation of tunnels. However, the diversity and scarcity of abnormal data make it difficult to identify outliers, especially to diagnose structural anomalies from poor-quality data. Therefore, an adaptive loss function improved autoencoder (AdaAE) model is proposed for anomaly detection, which is robust to poor-quality data and can accurately determine the anomaly source of river tunnel. To expand the abnormal dataset, numerical simulation of structure under extreme conditions and Gaussian noise are adopted to construct structural damage data and disturbance data respectively. The proposed model is then instantiated on the prepared dataset. Finally, the reliability and the advantage of the proposed model are verified by ablation study. The research results indicate that the detection ability of AdaAE model is greatly improved to that of the widely used methods. This model is suitable to poor quality dataset, and the accuracy to detect structural anomalies from pollution data sets is more than 90%. As a case study, the AdaAE model is applied to the Wuhan Yangtze River tunnel to detect anomalies of segment strain. This study would play a role in preventing structural diseases and promoting intelligent management during tunnel operation. |
| 资助项目 | National Key R&D Program of China[2021YFC3100805] ; Key project in Hubei Province[2023BCB048] ; National Natural Science Foundation of China[42293355] ; National Natural Science Foundation of China[51991392] ; Key deployment projects of Chinese Academy of Sciences[ZDRW-ZS-2021-3] ; Project for Research Assistant of Chinese Academy of Sciences |
| WOS研究方向 | Computer Science ; Engineering ; Operations Research & Management Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001145803000001 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/40382]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Chen, Weizhong |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 3.Univ Malaya, Dept Comp Syst & Technol, Kuala Lumpur, Malaysia 4.Beihang Univ, SKLSDE Lab, Beijing 100191, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tan, Xu-Yan,Palaiahnakote, Shivakumara,Chen, Weizhong,et al. A novel autoencoder for structural anomalies detection in river tunnel operation[J]. EXPERT SYSTEMS WITH APPLICATIONS,2024,244:14. |
| APA | Tan, Xu-Yan,Palaiahnakote, Shivakumara,Chen, Weizhong,Cheng, Ke,&Du, Bowen.(2024).A novel autoencoder for structural anomalies detection in river tunnel operation.EXPERT SYSTEMS WITH APPLICATIONS,244,14. |
| MLA | Tan, Xu-Yan,et al."A novel autoencoder for structural anomalies detection in river tunnel operation".EXPERT SYSTEMS WITH APPLICATIONS 244(2024):14. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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