Outlier Detection Based on Multivariable Panel Data and K-Means Clustering for Dam Deformation Monitoring Data
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 da...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/3739551 |
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| author | Jintao Song Shengfei Zhang Fei Tong Jie Yang Zhiquan Zeng Shuai Yuan |
| author_facet | Jintao Song Shengfei Zhang Fei Tong Jie Yang Zhiquan Zeng Shuai Yuan |
| author_sort | Jintao Song |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3f9fca867d5441d8a60f3917d3658b96 |
| institution | Kabale University |
| issn | 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-3f9fca867d5441d8a60f3917d3658b962025-02-03T05:59:58ZengWileyAdvances in Civil Engineering1687-80942021-01-01202110.1155/2021/3739551Outlier Detection Based on Multivariable Panel Data and K-Means Clustering for Dam Deformation Monitoring DataJintao Song0Shengfei Zhang1Fei Tong2Jie Yang3Zhiquan Zeng4Shuai Yuan5School of Water Resources and Hydro-Electric EngineeringSchool of Water Resources and Hydro-Electric EngineeringSchool of Water Resources and Hydro-Electric EngineeringSchool of Water Resources and Hydro-Electric EngineeringPowerchina Huadong Engineering CorporationSchool of Water Resources and Hydro-Electric EngineeringA 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.http://dx.doi.org/10.1155/2021/3739551 |
| spellingShingle | Jintao Song Shengfei Zhang Fei Tong Jie Yang Zhiquan Zeng Shuai Yuan Outlier Detection Based on Multivariable Panel Data and K-Means Clustering for Dam Deformation Monitoring Data Advances in Civil Engineering |
| title | Outlier Detection Based on Multivariable Panel Data and K-Means Clustering for Dam Deformation Monitoring Data |
| title_full | Outlier Detection Based on Multivariable Panel Data and K-Means Clustering for Dam Deformation Monitoring Data |
| title_fullStr | Outlier Detection Based on Multivariable Panel Data and K-Means Clustering for Dam Deformation Monitoring Data |
| title_full_unstemmed | Outlier Detection Based on Multivariable Panel Data and K-Means Clustering for Dam Deformation Monitoring Data |
| title_short | Outlier Detection Based on Multivariable Panel Data and K-Means Clustering for Dam Deformation Monitoring Data |
| title_sort | outlier detection based on multivariable panel data and k means clustering for dam deformation monitoring data |
| url | http://dx.doi.org/10.1155/2021/3739551 |
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