Missing data imputation in tunnel monitoring with a spatio-temporal correlation fused machine learning model
文献类型：期刊论文
| 作者 | Tan, Xuyan3; Chen, Weizhong3; Tan, Xianjun3; Fan, Chengkai1; Mao, Yuhao2; Cheng, Ke2; Du, Bowen2 |
| 刊名 | JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING
 |
| 出版日期 | 2024-12-02 |
| 页码 | 12 |
| 关键词 | Tunnel Machine learning Monitoring Missing data Imputation |
| ISSN号 | 2190-5452 |
| DOI | 10.1007/s13349-024-00877-8 |
| 英文摘要 | Imputing missing values in structural health monitoring (SHM) data is essential for conducting data-driven analyses of tunnel structural stability. However, SHM data is dynamically changing with complex spatio-temporal correlations, making it particularly challenging to impute, especially for continuous or peak missing data. Therefore, this study aims to present a spatio-temporal correlations fused machine learning model (ST-ML) to accurately impute different forms of missing SHM data. Unlike existing methods, this approach considers various physical scenarios by incorporating Gaussian distribution and employing a bidirectional recursion structure to enhance the robustness of the model. Consequently, a series of ablation experiments and comparative analyses were conducted using SHM data from a case study to evaluate the rationale and necessity of the ST-ML model, as well as its imputation ability across datasets with varying levels of missing data. The experimental results demonstrate a significant improvement in imputation accuracy, with MAE and RMSE values reduced by at least 1.51 and 2.27, respectively, after considering spatio-temporal correlations and diverse physical cases. Moreover, the proposed model outperformed the commonly used models even under special cases, where the average imputation error was reduced by at least 1.15. These findings affirm the reliability of the proposed model. |
| 资助项目 | National Natural Science Foundation of China[2021YFC3100805] ; National Key R&D Program of China[2023BCB048] ; Key project in Hubei Province[42293355] ; Key project in Hubei Province[51991392] ; Science Foundation of China ; Project for Research Assistant of Chinese Academy |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001367933600001 |
| 出版者 | SPRINGER HEIDELBERG |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/43311]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Tan, Xianjun |
| 作者单位 | 1.Univ Alberta, Dept Civil & Environm Engn, Edmonton, AB T6G 2E3, Canada 2.Beihang Univ, SKLSDE 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 |
| 推荐引用方式 GB/T 7714 | Tan, Xuyan,Chen, Weizhong,Tan, Xianjun,et al. Missing data imputation in tunnel monitoring with a spatio-temporal correlation fused machine learning model[J]. JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING,2024:12. |
| APA | Tan, Xuyan.,Chen, Weizhong.,Tan, Xianjun.,Fan, Chengkai.,Mao, Yuhao.,...&Du, Bowen.(2024).Missing data imputation in tunnel monitoring with a spatio-temporal correlation fused machine learning model.JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING,12. |
| MLA | Tan, Xuyan,et al."Missing data imputation in tunnel monitoring with a spatio-temporal correlation fused machine learning model".JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING (2024):12. |
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。