Comparison of K-means and FCM algorithms to optimize spatiotemporal pore pressure prediction of earth dams - Beiranvand, Behrang; Rajaee, Taher; Komasi, Mehdi (Results in engineering, 2024)
•This research shows that the prediction of the pore pressure zoning of earth dams using FCM and k-means algorithms in terms of the formation of homogeneous areas and acceptable estimates can be a suitable method for zoning and analyzing the pore pressure of dams.•By comparing the results of the clustering algorithms, it was found that the FCM algorithm has more suitable results than the k-means clustering algorithm due to the uncertainties in determining the boundaries of the classes.•The proposed method can reduce the collinearity between observations and provide a more accurate analysis of monitored points in spatial and temporal conditions. 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.