Machine learning-based multi-class classification of rock mass permeability using integrated geotechnical and geophysical parameters

In engineering applications, machine learning (ML) provides a promising approach when direct characterization of subsurface properties is challenging, time-consuming, and costly. This study developed a multi-class ML framework for classifying rock mass permeability based on Lugeon values using 86 observations from drilling and geophysical investigations at the Kırklartepe dam site in Bayburt, Türkiye. Seven predictors were considered: depth, Rock Quality Designation (RQD), joint frequency, Schmidt hammer hardness, P-wave velocity, S-wave velocity, and electrical resistivity. The observations were assigned to four permeability classes: very low, low, moderate, and high. Six classifiers were evaluated: Polynomial Kernel Support Vector Machine (P-SVM), Linear Discriminant Analysis, k-Nearest Neighbors (k-NN), AdaBoostM2, Bagged Trees, and Radial Basis Function SVM. Sequential Forward Feature Selection and Leave-One-Out Cross-Validation were used for feature selection and model evaluation, respectively. Robustness was further assessed using bootstrap confidence intervals, class-imbalance treatments, feature-selection stability, and learning curves. Among the baseline models, P-SVM using all seven predictors achieved the highest accuracy (79.07%), with a macro-F1 of 0.774 and the lowest Golden Distance (GD = 0.4372). When balanced class-wise performance was prioritized, k-NN with in-fold SMOTE achieved the lowest overall GD (0.4126) and a macro-F1 of 0.777. S-wave velocity and electrical resistivity were the most influential predictors. Overall, the findings demonstrate that the proposed framework effectively integrates geotechnical and geophysical data for rock mass permeability classification, providing a robust, data-driven decision-support approach that supports subsurface characterization and complements conventional permeability assessment in engineering applications.