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Outlier Detection Based on Multivariable Panel Data and K-Means Clustering for Dam Deformation Monitoring Data

Outlier Detection Based on Multivariable Panel Data and K-Means Clustering for Dam Deformation Monitoring Data

Authors :
Jintao Song
Shengfei Zhang
Fei Tong
Jie Yang
Zhiquan Zeng
Shuai Yuan
Source :
Advances in Civil Engineering, Vol 2021 (2021)
Publication Year :
2021
Publisher :
Hindawi Limited, 2021.

Abstract

A dam is a super-structure widely used in water conservancy engineering fields, and its long-term safety is a focus of social concern. Deformation is a crucial evaluation index and comprehensive reflection of the structural state of dams, and thus there are many research papers on dam deformation data analysis. However, the accuracy of deformation data is the premise of dam safety monitoring analysis, and original deformation data may have some outliers caused by manual errors or instruments aging after long-time running. These abnormal data have a negative impact on the evaluation of dam structural safety. In this study, an analytical method for detecting outliers of dam deformation data was established based on multivariable panel data and K-means clustering theory. First, we arranged the original spatiotemporal monitoring data into the multivariable panel data format. Second, the correlation coefficients between the deformation signals of different measuring points were studied based on K-means clustering theory. Third, the outlier detection rules were established through the changes of the correlation coefficients. Finally, the proposed model was applied to the Jinping-I Arch Dam in China which is the highest dam in the world, and results indicate that the detection method has high accuracy detection ability, which is valuable in dam safety monitoring applications.

Details

Language :
English
ISSN :
16878094
Volume :
2021
Database :
Directory of Open Access Journals
Journal :
Advances in Civil Engineering
Publication Type :
Academic Journal
Accession number :
edsdoj.3f9fca867d5441d8a60f3917d3658b96
Document Type :
article
Full Text :
https://doi.org/10.1155/2021/3739551