Neural network for rock slope stability evaluation

Abstract

The Yudonghe landslide, located in western Hubei Province of China, consists of eastern and western subunits as well as a main landslide mass with upper and lower slip surfaces. As an important landslide close to Shuibuya Dam on the Qing River, its stability is crucial, as the slide might reactivate because of a change in ground-water level caused by filling of the Shuibuya Reservoir. Existing weakness zones, growth of ruptures, the downslope attitude of geologic strata, and water infiltration, which reduced the strength of rocks and soils, have been found to be the most important factors contributing to the Yudonghe landslide. With regard to the landslide processes, it can be noted that the original large-scale slide activity was due to erosion by the Qing River, the second sliding resulted from the fall of blocks from the head scarp, and the final activity was the growth of the eastern and western secondary slides. A base failure was the main type of slope movement, however, it was obvious that more than one sliding event occurred, as inferred from striations and fractures detected by microstructure analysis of soils along the failure surfaces. Slope instability was evaluated by the method of Back Propagation Neural Networks (BPNN), in which a four-layer BPNN model with five input nodes, two hidden layers, and two output nodes was constructed using a training data set of landslide samples throughout the Qing River area. The predicted results of this analysis showed that the factor of safety was 1.10, which indicates that the Yudonghe landslide is currently in a marginally stable condition.