A new approach to rock mass classification using machine learning algorithms

This paper proposes a new model for rock mass classification, using machine learning techniques. The variables used are often associated with well-known geomechanical classifications. Factor analysis permitted reducing system dimensionality, selecting only the significant variables related to the rock mass quality. The proposed model used the k-medoid clustering technique to create the geomechanical classes, specifically the partitioning around medoids algorithm, unsupervised learning. The results indicated the formation of seven groups, labeled through the interpretation of the variable values in each group. Subsequently, a decision tree was applied to obtain the geomechanical classes defined by k-medoids technique; aiming to present an easy way for new users to apply the proposed rock mass classification. The proposed model is a new approach to deal with rock mass classification problems, decreasing subjectivity, increasing parameter selectivity, reducing the dimensionality of geotechnical databases and optimizing the application of classification systems.