Comparison of K-means and FCM algorithms to optimize spatiotemporal pore pressure prediction of earth dams
To identify the behavior and health monitoring of dams, it is necessary to correctly interpret the results of instrumentation in different phases of construction, impounding, and operation. Therefore, the approach based on spatiotemporal prediction is presented to improve the interpretation of pore...
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| Language: | English |
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S259012302401630X |
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| author | Behrang Beiranvand Taher Rajaee Mehdi Komasi |
| author_facet | Behrang Beiranvand Taher Rajaee Mehdi Komasi |
| author_sort | Behrang Beiranvand |
| collection | DOAJ |
| description | To identify the behavior and health monitoring of dams, it is necessary to correctly interpret the results of instrumentation in different phases of construction, impounding, and operation. Therefore, the approach based on spatiotemporal prediction is presented to improve the interpretation of pore pressure behavior of Eyvashan Earth Dam. In this research, using the results of other existing healthy piezometers, a spatiotemporal distribution model is proposed using panel data, which can be effective for predicting and reconstructing missing data. The optimal spatiotemporal clustering of pore pressure changes monitoring with K-Means and Fuzzy C-means (FCM) algorithms will enable the monitoring of points of the dam where instrumentations are not designed and installed or defective instrumentations. In predicting the pore pressure of dams, the input data is classified based on the pore pressure monitoring data, but with the use of clustering algorithms, the classification after the cluster analysis steps will lead to the proper resolution of the pore pressure clustering. According to the validation results of each of the clustering algorithms, the FCM clustering algorithm has more suitable results than the K-Means algorithm in determining the pore pressure clusters. In general, FCM clustering and K-Means algorithms are suitable and efficient tools in the field of more accurate monitoring of earth dams, and by using the proposed method, the detection of unusual areas of pore pressure and the related safety diagnosis is facilitated. |
| format | Article |
| id | doaj-art-ae2e1236308c4a6d99bae052acadbb08 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-ae2e1236308c4a6d99bae052acadbb082024-12-19T10:59:12ZengElsevierResults in Engineering2590-12302024-12-0124103377Comparison of K-means and FCM algorithms to optimize spatiotemporal pore pressure prediction of earth damsBehrang Beiranvand0Taher Rajaee1Mehdi Komasi2Department of Civil Engineering, University of Qom, Qom, Iran; Corresponding author.Department of Civil Engineering, University of Qom, Qom, IranDepartment of Civil Engineering, University of Ayatollah, Ozma, Boroujerdi, IranTo identify the behavior and health monitoring of dams, it is necessary to correctly interpret the results of instrumentation in different phases of construction, impounding, and operation. Therefore, the approach based on spatiotemporal prediction is presented to improve the interpretation of pore pressure behavior of Eyvashan Earth Dam. In this research, using the results of other existing healthy piezometers, a spatiotemporal distribution model is proposed using panel data, which can be effective for predicting and reconstructing missing data. The optimal spatiotemporal clustering of pore pressure changes monitoring with K-Means and Fuzzy C-means (FCM) algorithms will enable the monitoring of points of the dam where instrumentations are not designed and installed or defective instrumentations. In predicting the pore pressure of dams, the input data is classified based on the pore pressure monitoring data, but with the use of clustering algorithms, the classification after the cluster analysis steps will lead to the proper resolution of the pore pressure clustering. According to the validation results of each of the clustering algorithms, the FCM clustering algorithm has more suitable results than the K-Means algorithm in determining the pore pressure clusters. In general, FCM clustering and K-Means algorithms are suitable and efficient tools in the field of more accurate monitoring of earth dams, and by using the proposed method, the detection of unusual areas of pore pressure and the related safety diagnosis is facilitated.http://www.sciencedirect.com/science/article/pii/S259012302401630XMissing dataPore pressure, InstrumentationPanel dataK-means algorithmFCM algorithm |
| spellingShingle | Behrang Beiranvand Taher Rajaee Mehdi Komasi Comparison of K-means and FCM algorithms to optimize spatiotemporal pore pressure prediction of earth dams Results in Engineering Missing data Pore pressure, Instrumentation Panel data K-means algorithm FCM algorithm |
| title | Comparison of K-means and FCM algorithms to optimize spatiotemporal pore pressure prediction of earth dams |
| title_full | Comparison of K-means and FCM algorithms to optimize spatiotemporal pore pressure prediction of earth dams |
| title_fullStr | Comparison of K-means and FCM algorithms to optimize spatiotemporal pore pressure prediction of earth dams |
| title_full_unstemmed | Comparison of K-means and FCM algorithms to optimize spatiotemporal pore pressure prediction of earth dams |
| title_short | Comparison of K-means and FCM algorithms to optimize spatiotemporal pore pressure prediction of earth dams |
| title_sort | comparison of k means and fcm algorithms to optimize spatiotemporal pore pressure prediction of earth dams |
| topic | Missing data Pore pressure, Instrumentation Panel data K-means algorithm FCM algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S259012302401630X |
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