Study on slope deformation partition and monitoring point optimization considering spatial correlation

Slope displacement prediction is an effective means to realize slope disaster early warning and prediction. The scientific and reasonable layout of surface displacement monitoring points is the basis of slope disaster prediction. There are some defects in the determination of the number and location of monitoring points in the process of laying, and there needs to be an effective regulation on how to lay monitoring points according to the overall deformation characteristics of the slope. Therefore, combining the displacement data sequence and spatial location information of the surface displacement monitoring points, based on the correlation of the displacement sequence between different monitoring points, quantifying the spatial correlation of the monitoring points, introducing the normalized spatial correlation scale index, using the cluster analysis method considering the spatial correlation of the monitoring points to divide the deformation area of the slope, and removing the redundant monitoring points in each deformation area according to the result of the deformation area division, Finally, the overall optimization of slope monitoring points is realized. Through this method, slope deformation zoning is more scientific and reasonable, avoiding the influence of human subjectivity on the site and scientifically solving the data overlap phenomenon caused by the unreasonable arrangement of surface displacement monitoring points, thus improving 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 southwest China was used as the research object. And 24 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.