Identification of anomaly of tunnel segment strain using an adaptive machine learning model
文献类型：期刊论文
| 作者 | Tan, Xu-Yan2,3; Tan, Xian-Jun2,3; Zhang, Rui1; Zhang, Zhixin4; Tarek, Zayd5; Du, Bo-Wen4 |
| 刊名 | GEORISK-ASSESSMENT AND MANAGEMENT OF RISK FOR ENGINEERED SYSTEMS AND GEOHAZARDS
 |
| 出版日期 | 2024-08-29 |
| 页码 | 14 |
| 关键词 | Tunnel machine learning anomaly identification simulation outliers |
| ISSN号 | 1749-9518 |
| DOI | 10.1080/17499518.2024.2395554 |
| 英文摘要 | Anomaly identification is a crucial issue for preventing diseases in tunnel structures. However, distinguishing whether the structure is abnormal from outliers is challenging, as the monitoring dataset composed of time series may contain non-structural abnormal data caused by operational environmental pollution. Therefore, this study aims to propose a structural anomaly identification model to distinguish the structural anomaly from the polluted dataset based on an improved autoencoder model with adaptive loss function modification (AAE). To solve the problem of insufficient abnormal samples in practice, the proposed model is applied to a time series dataset obtained from numerical simulation, where extreme conditions are introduced to generate structural abnormal data, and Gaussian noises are overlaid to pollute the raw data and generate non-structural abnormal data. Then, the anomaly identification capability of the AAE model under different intensities of noise pollution conditions is discussed, along with its reliability compared to other common methods. Experimental results demonstrate that the AAE model can effectively identify structural anomaly information from heavily polluted datasets. The accuracy of the AAE model improved by at least 4% to other models even on a serious polluted dataset. Therefore, the structural anomaly identification model presented in this study is reliable. |
| 资助项目 | National Key R&D Program of China[2021YFC3100805] ; Key project in Hubei Province[2023BCB048] ; Project for Research Assistant of Chinese Academy of Sciences ; National Natural Science Foundation of China[42293355] ; National Natural Science Foundation of China[51991392] ; Science and Technology Planing Project of Tibet Autonomous Region[XZ202201ZY0021G] |
| WOS研究方向 | Engineering ; Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:001299588400001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/42366]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Tan, Xian-Jun |
| 作者单位 | 1.Nanyang Technol Univ, Singapore, Singapore 2.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Beihang Univ, SKLSDE Lab, Beijing, Peoples R China 5.Hong Kong Polytech Univ, Dept Bldg & Real Estate, Hong Kong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tan, Xu-Yan,Tan, Xian-Jun,Zhang, Rui,et al. Identification of anomaly of tunnel segment strain using an adaptive machine learning model[J]. GEORISK-ASSESSMENT AND MANAGEMENT OF RISK FOR ENGINEERED SYSTEMS AND GEOHAZARDS,2024:14. |
| APA | Tan, Xu-Yan,Tan, Xian-Jun,Zhang, Rui,Zhang, Zhixin,Tarek, Zayd,&Du, Bo-Wen.(2024).Identification of anomaly of tunnel segment strain using an adaptive machine learning model.GEORISK-ASSESSMENT AND MANAGEMENT OF RISK FOR ENGINEERED SYSTEMS AND GEOHAZARDS,14. |
| MLA | Tan, Xu-Yan,et al."Identification of anomaly of tunnel segment strain using an adaptive machine learning model".GEORISK-ASSESSMENT AND MANAGEMENT OF RISK FOR ENGINEERED SYSTEMS AND GEOHAZARDS (2024):14. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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