Back-propagation neural network for assessment of highway slope failure in Taiwan

The highway slope failure during typhoons and earthquakes is a major geotechnical engineering problem in Taiwan. Intensive studies have been carried out in recent years. However, conventional investigations for determining slope failure have focused on the linear relationships between the dominant factors, such as slope angle, slope height, material, construction, rainfall, earthquake and etc., although it should be a complicated nonlinear relationship. In this paper, a back-propagation neural network is proposed as an assessment tool for the slope failure. On-site slope failure data at the A-Li-San Highway in southern Taiwan are used to test the performance of the artificial neural network model. The numerical results demonstrate the effectiveness of the artificial neural network in the evaluation of slope failure potential with five major factors, such as slope gradient angle, slope height, cumulative precipitation, surface acceleration and strength of material.