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Toward Automated and Refined Rock Mass Quality Evaluation: An AR-RMR System Driven by AI and Multi-source Heterogeneous Data
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DOI：10.1007/s00603-026-05880-7.png)
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Abstract
En 中文
Rock mass quality evaluation provides the fundamental basis for stability analysis, support design, and engineering decision-making. However, traditional empirical methods mainly rely on localized field investigations and subjective interpretation, resulting in limited objectivity and spatial representativeness. To overcome these limitations, this study proposes an automated and refined rock mass rating (AR-RMR) system integrating validated artificial intelligence (AI) modules with multi-source heterogeneous data. The AR-RMR framework provides three major capabilities: (1) AI-assisted extraction of key RMR parameters through automated recognition of planar and linear discontinuities; (2) refined quantification of key parameters, including rock quality designation, spacing, persistence, aperture, roughness, infilling, weathering degree, and groundwater condition; and (3) integration of automated parameter extraction, refined parameter quantification, and RMR scoring into a unified rock mass quality evaluation workflow, thereby improving the objectivity and spatial representativeness of conventional RMR assessment. Validation on two representative tunnel sections demonstrates good agreement between AR-RMR and conventional field evaluation. Further application to 47 tunnel sections yields a coefficient of determination (R2) of 0.729 and a root mean square error (RMSE) of 3.656 compared with field RMR, while independent validation using geological radar prediction further confirms the reliability of the proposed framework. The proposed AR-RMR provides an effective pathway toward objective, refined, and spatially representative rock mass quality evaluation, offering reliable technical support for stability assessment, support design, and engineering decision-making in rock engineering.
Keywords:
Rock mass quality
AR-RMR system
AI-based parameter extraction
Global quantitative assessment
Spatial representativeness
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