Abstract: A dam slope deformation monitoring system and method are provided. The monitoring system monitors an entire dam in a reservoir area by using an unmanned aerial vehicle (UAV) photogrammetry system, and determines an encrypted monitoring area (steep slope) with the relatively large deformation and a relatively large digital elevation difference; determines, in the intensive monitoring area, a first level key monitoring area with the larger deformation by using a ground-based radar interferometry measurement system; determines, in the first level key monitoring area, a second level key monitoring area with the larger deformation by using a ground-based three-dimensional lidar measurement system; determines, in the second level key monitoring area, a key monitoring particle with a high deformation speed by using a global navigation satellite system (GNSS). The core chip stack is used to monitor and warn the collapse process in the area where the key monitoring particles are located.

Abstract: The present disclosure provides a method and a system for precise location of a high slope collapse area. Firstly, the slope images in a long time series are obtained, the slope images in the long time series are composed into a two-dimensional slope deformation graph, and an area with the maximum deformation in the two-dimensional slope deformation graph is selected as a deformation area. Then, the deformation area is segmented by straight line, and the deformation region obtained by straight line segmentation is displayed in overlapping way in the slope images of long time series, and the region corresponding to the connecting line with the largest change range is selected as the monitoring line area from the overlapping image.

Abstract: The present invention provides a prediction method and system of high slope deformation. First, historical deformation data of each period of each part of a high slope is obtained as sample data; the sample data is divided into training samples and test samples; then a parameter group of a Support Vector Machine (SVM) model is optimized by using the training samples and a particle Swarm Optimization (PSO) algorithm to determine an optimal parameter group of the SVM model, to obtain a trained SVM model; whether the trained SVM model satisfies a condition is verified by using the test samples, and when the SVM model does not satisfy the condition, an optimal parameter group of the SVM model is re-determined; and finally the deformation of each area of the high slope is predicted by using the SVM model that satisfies the condition.

Abstract: A method for cemented material dam construction based on whole-process quality control includes: determining a water-binder ratio of a sand and gravel material through a simulation test; laying rockfill on the first cemented sand and gravel layer before the final setting time of the first cemented sand and gravel layer; adjusting the maximum particle size of the rockfill and the distance between the particles in the rockfill according to the design requirements; spraying a cement slurry on the rockfill to wrap all the particles in the rockfill and laying a second cemented sand and gravel layer before the initial setting time of the cement slurry; performing rolling compaction on the construction layer until the decline in the thickness of the construction layer and the apparent density of the rolled surface meet the design requirements; and completing the cemented material dam construction.