A Safety Monitoring Method for Anchor Cable Prestress During Slope Construction Based on Spatial Clustering and a Bayesian Panel Vector Autoregression Model

During the construction of water conservancy and hydropower projects, the time series of safety monitoring data for slope engineering is often short. When establishing a slope safety monitoring model, it is necessary to study the modelling method under small sample conditions and closely examine the space–time information in the monitoring data for the limited length of each measuring point. To this end, this paper initially proposes using the Ward clustering method to spatially cluster anchor cable prestress measuring points in different parts of a slope; then, according to the clustering results, a safety monitoring model for slope anchor cable prestress is established for each type of measuring point based on a Bayesian panel vector autoregressive (BPVAR) model with highly accurate small sample modelling. Finally, slope anchor cable prestress monitoring data in China are taken as examples for verification analysis. The analysis results show that the prestressed measuring points of the slope anchor cable for this project are divided into four categories: excavation causes the prestress of some slope measuring points to continue to increase, and the tension or compression of the structural plane leads to an increase or decrease in prestress. The multiple correlation coefficients of the training set and test set data of the BPVAR model are all above 0.80, and the prediction error of the validation set is less than that of the vector autoregressive (VAR) model, the autoregressive moving average (ARMA) model and the long short-term memory (LSTM) model. The measured prestress values are all within the 95% confidence interval, which provides a reference for safety state identification in slope engineering.