Investigation on identification of structural anomalies from polluted data sets using an unsupervised learning method
文献类型：期刊论文
| 作者 | Ye, Junchen4; Zhang, Zhixin2; Cheng, Ke2; Tan, Xuyan1,3; Du, Bowen4; Chen, Weizhong1,3 |
| 刊名 | FRONTIERS OF STRUCTURAL AND CIVIL ENGINEERING
 |
| 出版日期 | 2024-09-20 |
| 页码 | 13 |
| 关键词 | structural health monitoring deep learning anomaly detection |
| ISSN号 | 2095-2430 |
| DOI | 10.1007/s11709-024-1065-3 |
| 英文摘要 | Civil infrastructure is prone to structural damage due to high geo-stress and other natural disasters, so monitoring is required. Data collected by structural health monitoring (SHM) systems are easily affected by many factors, such as temperature, sensor fluctuation, sensor failure, which can introduce a lot of noise, increasing the difficulty of structural anomaly identification. To address this problem, this paper designs a new process of structural anomaly identification under noisy conditions and offers Civil Infrastructure Denoising Autoencoder (CIDAE), a denoising autoencoder-based deep learning model for SHM of civil infrastructure. As a case study, the effectiveness of the proposed model is verified by experiments on deformation stress data of the Wuhan Yangtze River Tunnel based on finite element simulation. Investigation of the circumferential weld and longitudinal weld data of the case study is also conducted. It is concluded that CIDAE is superior to traditional methods. |
| 资助项目 | National Natural Science Foundation of China[51991395] ; National Natural Science Foundation of China[51991391] ; National Natural Science Foundation of China[U1811463] ; S&T Program of Hebei, China[225A0802D] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001316747300002 |
| 出版者 | HIGHER EDUCATION PRESS |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/42555]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Du, Bowen |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Beihang Univ, CCSE Lab, Beijing 100191, Peoples R China 3.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 4.Beihang Univ, Sch Transportat Sci & Engn, Beijing 100191, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ye, Junchen,Zhang, Zhixin,Cheng, Ke,et al. Investigation on identification of structural anomalies from polluted data sets using an unsupervised learning method[J]. FRONTIERS OF STRUCTURAL AND CIVIL ENGINEERING,2024:13. |
| APA | Ye, Junchen,Zhang, Zhixin,Cheng, Ke,Tan, Xuyan,Du, Bowen,&Chen, Weizhong.(2024).Investigation on identification of structural anomalies from polluted data sets using an unsupervised learning method.FRONTIERS OF STRUCTURAL AND CIVIL ENGINEERING,13. |
| MLA | Ye, Junchen,et al."Investigation on identification of structural anomalies from polluted data sets using an unsupervised learning method".FRONTIERS OF STRUCTURAL AND CIVIL ENGINEERING (2024):13. |
入库方式： OAI收割
来源：武汉岩土力学研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。