A STUDY ON PREDISPOSING FACTORS FOR ESTIMATING FAILURE PROBABILITY OF EXPRESSWAY CUT SLOPES UNDER HEAVY RAINFALL USING DEEP LEARNING

Abstract

This paper presents a study on validation of the training data for the predisposing factors of the slope failure in order to apply deep learning to the estimation of the probability of slope failure of cut-slopes on expressways due to heavy rainfall. In the previous study, a method for quantitative evaluation of slope failure risk was proposed by artificially weighting the features of slope predisposition. By contrast, to improve the objectivity and versatility of the dataset, the authors constructed the supervised learning with the objective dataset of only items without weighting of features, and compared the model with those of previous studies in terms of validation. The results indicated that the accuracy was improved in the latest failure cases when the training dataset was not weighted.