Safety Monitoring Method for the Uplift Pressure of Concrete Dams Based on Optimized Spatiotemporal Clustering and the Bayesian Panel Vector Autoregressive Model

Abstract

To establish a safety monitoring method for the uplift pressure of concrete dams, spatiotemporal information from monitoring data is needed. In the present study, the method of ordering points to identify the clustering structure is employed to spatially cluster the uplift pressure measuring points at different locations on the dam; three distance indexes and two clustering evaluation indexes are used to realize clustering optimization and select the optimal clustering results. The Bayesian panel vector autoregressive model is used to establish the uplift stress safety monitoring model for each category of monitoring point. For a nonstationary sequence, the difference method is selected to ensure that the sequence is stable, and the prediction is carried out according to the presence or absence of exogenous variables. The result is that the addition of exogenous variables increases the accuracy of the model’s forecast. Engineering examples show that the uplift pressure measurement points on the dam are divided into seven categories, and classification is based mainly on location and influencing factors. The multiple correlation coefficients of the training set and test set data of the BPVAR model are more than 0.80, and the prediction error of the validation set is lower than that of the Back Propagation neural network, XGBoost algorithm, and Support Vector Machines. The research in this paper provides some reference for seepage monitoring of concrete dams.