Stability condition identification of rock and soil cutting slopes based on soft computing

Abstract

For transportation infrastructure, one of the greatest challenges today is to keep large-scale transportation networks, such as railway networks, operational under all conditions. This task becomes even more difficult to accomplish if one takes into account budget limitations for maintenance and repair works. This paper presents a tool aimed at helping in management tasks related to maintenance and repair work for a particular element of this infrastructure, the slopes. The highly flexible learning capabilities of artificial neural networks (ANNs) and support vector machines (SVMs) were applied in the development of a tool able to identify the stability condition of rock and soil cutting slopes, keeping in mind the use of information usually collected during routine inspection activities (visual information) to feed the models. This task was addressed following two different strategies: nominal classification and regression. Moreover, to overcome the problem of imbalanced data, three training sampling approaches were explored: no resampling, synthetic minority oversampling technique (SMOTE), and oversampling. The achieved results are presented and discussed, comparing the performance of ANN and SVM algorithms as well as the effect of the sampling approaches. A comparison between nominal classification and regression strategies for both rock and soil cutting slopes is also carried out, highlighting the different performance observed in the study of the two different types of slope.