Data-driven classification approaches for stability condition prediction of soil cutting slopes

Abstract

For transportation infrastructures, one of the greatest challenges today is to keep large-scale transportation networks, such as railway networks, operational under all conditions. In this paper we present a tool aimed at helping in management tasks related to maintenance and repair works for a particular component of these infrastructures, the slopes. For that, the high and flexible learning capabilities of artificial neural networks and support vector machines were applied in the development of a tool able to identify the stability condition of 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, SMOTE and Oversampling. The achieved results are presented and discussed, comparing both algorithms performance as well as the effect of the sampling approaches. A comparison between nominal classification and regression strategies is also carried out. These achieved results can give a valuable contribution for practical applications at network level.