A Multidimensional Cloud Model with FDAHP–Objective Combined Weighting for Quantitative Rock Drillability Classification

Rock drillability classification provides an important basis for drilling-parameter optimization, equipment selection, and improved mining efficiency. Existing drillability evaluation methods often rely on fixed empirical weights and rigid grade boundaries, making it difficult to capture fuzzy transitions between adjacent grades under multi-indicator geological conditions or to explain classification deviations for boundary samples. This study proposes a quantitative rock drillability classification method that integrates FDAHP-based subjective weighting, objective weighting, and a multidimensional cloud model. A 12-indicator evaluation system is first established by considering rock physicomechanical properties, rock-mass structural conditions, and drilling-response characteristics. FDAHP is then used to derive subjective weights from judgment matrices provided by five experts, while the entropy weight method, CRITIC method, and coefficient of variation method are used to obtain objective weights. These weights are combined into a subjective–objective weighting scheme and incorporated into a multidimensional cloud model to represent the fuzziness and randomness of drillability grade boundaries. For incomplete-indicator samples, the comprehensive weights are projected onto the available indicator subset and renormalized, avoiding forced imputation of missing indicators. Validation using 15 complete samples and seven incomplete-indicator samples from the Sungun copper mine shows that the proposed combined weighting method achieves an accuracy of 93.33% for complete samples and correctly classifies six of seven incomplete-indicator samples, with an accuracy of 85.71%. The cloud-model analysis of the misclassified sample indicates that it lies near an adjacent-grade boundary, providing an interpretable explanation for its classification uncertainty. These results suggest that the proposed method can provide interpretable quantitative drillability classification for the Sungun case study and may support preliminary field drillability assessment under incomplete-information conditions.