Slope deformation partitioning and monitoring points optimization based on cluster analysis

The scientific and fair positioning of monitoring locations for surface displacement on slopes is a prerequisite for early warning and forecasting. However, there is no specific provision on how to effectively determine the number and location of monitoring points according to the actual deformation characteristics of the slope. There are still some defects in the layout of monitoring points. To this end, based on displacement data series and spatial location information of surface displacement monitoring points, by combining displacement series correlation and spatial distance influence factors, a spatial deformation correlation calculation model of slope based on clustering analysis was proposed to calculate the correlation between different monitoring points, based on which the deformation area of the slope was divided. The redundant monitoring points in each partition were eliminated based on the partition's outcome, and the overall optimal arrangement of slope monitoring points was then achieved. This method scientifically addresses the issues of slope deformation zoning and data gathering overlap. It not only eliminates human subjectivity from slope deformation zoning but also increases the efficiency and accuracy of slope monitoring. In order to verify the effectiveness of the method, a sand-mudstone interbedded Counter-Tilt excavation slope in the Chongqing city of China was used as the research object. Twenty-four monitoring points deployed on this slope were monitored for surface displacement for 13 months. The spatial location of the monitoring points was discussed. The results show that the proposed method of slope deformation zoning and the optimized placement of monitoring points are feasible.