Identifying the outlier in tunnel monitoring data: An integration model
文献类型：期刊论文
| 作者 | Liu, Jinquan; Zou, Tongtong |
| 刊名 | COMPUTER COMMUNICATIONS
 |
| 出版日期 | 2022-04-15 |
| 卷号 | 188页码:145 |
| 关键词 | Tunnel engineering Structural health monitoring Model integration Outlier detection Machine learning |
| ISSN号 | 0140-3664 |
| 英文摘要 | Structural health monitoring (SHM) system based on the Internet of Things is an important method to evaluate the safety of tunnel operation through the real-time monitoring data analysis. Identifying the outlier in SHM data is a non-trivial task but it is challenging for the tunnel engineers because the measurements are quite complicated with the characteristics of time series, unlabeled, high-dimensional, inter-correlations between variables, etc. To detect the outliers, an integration model is developed based on the independent data analysis from the Probabilistic, Proximity-Based (global), Proximity-Based (local), Linear Model and Outlier Ensembles. The model is examined with the shuttle data set in the University of California Irvine (UCI) database and its precision rate is up to 94.5%, highlighting the favorable performance in identifying the outliers. This method is thus applied to the outlier detection of the SHM data in the Nanjing Yangtze River tunnel. 6698 data sets collected from SHM are evaluated and 270 groups of outliers are identified effectively. By eliminating these outliers, comparisons between the proposed integrated model and the single model (i.e. IForest, ABOD, KNN, LOF) are further conducted to discuss the model performance based on the regression analysis. Results show that the integrated model is better than the single model and it possesses the great potential to detect the outliers in SHM system. |
| 学科主题 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000805827900012 |
| 出版者 | ELSEVIER |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/34730]  |
| 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.Lanzhou Jiaotong University |
| 推荐引用方式 GB/T 7714 | Liu, Jinquan,Zou, Tongtong. Identifying the outlier in tunnel monitoring data: An integration model[J]. COMPUTER COMMUNICATIONS,2022,188:145. |
| APA | Liu, Jinquan,&Zou, Tongtong.(2022).Identifying the outlier in tunnel monitoring data: An integration model.COMPUTER COMMUNICATIONS,188,145. |
| MLA | Liu, Jinquan,et al."Identifying the outlier in tunnel monitoring data: An integration model".COMPUTER COMMUNICATIONS 188(2022):145. |
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。