Investigation on the data augmentation using machine learning algorithms in structural health monitoring information
文献类型：期刊论文
| 作者 | Tan, Xuyan1,2; Sun, Xuanxuan3; Chen, Weizhong1,2; Du, Bowen3; Ye, Junchen3; Sun, Leilei3 |
| 刊名 | STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
 |
| 出版日期 | 2021-07-01 |
| 卷号 | 20期号:4页码:2054-2068 |
| 关键词 | Machine learning data mining supervised learning structural health monitoring data augmentation |
| ISSN号 | 1475-9217 |
| DOI | 10.1177/1475921721996238 |
| 英文摘要 | Structural health monitoring system plays a vital role in smart management of civil engineering. A lot of efforts have been motivated to improve data quality through mean, median values, or simple interpolation methods, which are low-precision and not fully reflected field conditions due to the neglect of strong spatio-temporal correlations borne by monitoring datasets and the thoughtless for various forms of abnormal conditions. Along this line, this article proposed an integrated framework for data augmentation in structural health monitoring system using machine learning algorithms. As a case study, the monitoring data obtained from structural health monitoring system in the Nanjing Yangtze River Tunnel are selected to make experience. First, the original data are reconstructed based on an improved non-negative matrix factorization model to detect abnormal conditions occurred in different cases. Subsequently, multiple supervised learning methods are introduced to process the abnormal conditions detected by non-negative matrix factorization. The effectiveness of multiple supervised learning methods at different missing ratios is discussed to improve its university. The experimental results indicate that non-negative matrix factorization can recognize different abnormal situations simultaneously. The supervised learning algorithms expressed good effects to impute datasets under different missing rates. Therefore, the presented framework is applied to this case for data augmentation, which is crucial for further analysis and provides an important reference for similar projects. |
| WOS研究方向 | Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:000667797200041 |
| 出版者 | SAGE PUBLICATIONS LTD |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/27359]  |
| 专题 | 中科院武汉岩土力学所 |
| 作者单位 | 1.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Beihang Univ, State Key Lab Software Dev Environm, Beijing 100083, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tan, Xuyan,Sun, Xuanxuan,Chen, Weizhong,et al. Investigation on the data augmentation using machine learning algorithms in structural health monitoring information[J]. STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL,2021,20(4):2054-2068. |
| APA | Tan, Xuyan,Sun, Xuanxuan,Chen, Weizhong,Du, Bowen,Ye, Junchen,&Sun, Leilei.(2021).Investigation on the data augmentation using machine learning algorithms in structural health monitoring information.STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL,20(4),2054-2068. |
| MLA | Tan, Xuyan,et al."Investigation on the data augmentation using machine learning algorithms in structural health monitoring information".STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 20.4(2021):2054-2068. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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