Representative elementary volume-enabled quality evaluation for complex structural rock masses: From the analytical perspective of the connectivity-controlled non-persistent fracture networks

Rock mass quality evaluation is a critical preliminary step in rock engineering, yet conventional methods often overlook the analytical connectivity of non-persistent fracture networks, reducing their precision and scope. To bridge this gap, this study proposes a novel connectivity-based geological strength index (CGSI) for rock mass quality assessment and establishes explicit and quantitative conversion relationships between different classification systems, thereby improving reliability across diverse engineering contexts. Using the representative elementary volume (REV) as the fundamental scale, an extensive quality evaluation of homogeneous domain #7 on a high and steep slope was conducted. Samples were optimized based on the REV, and four established assessment systems were applied to formulate conversion expressions. By integrating their core principles, we developed CGSI and established twelve quantitative conversion relationships spanning different rock strength ranges. CGSI's novelty lies in its capacity to quantify rock mass quality at the REV scale by incorporating surface conditions, structural distribution, fracture connectivity and fracture orientation effects to capture the three-dimensional fracture network. Field investigations and comparative analyses validated CGSI, identifying grade III as the optimal quality for domain #7. Results demonstrate CGSI's superior conversion accuracy for fractured rock masses and its strong applicability in engineering practice. Extending the method to other homogeneous domains yielded overall quality ratings of II ∼ IV. Information entropy analysis revealed that the number of moderately dipping fracture sets, REV size and mean fracture size are the dominant controlling factors. Notably, domains #1, #4, #6, #8, #12 and #14 were classified as grade IV, indicating elevated instability risks and the need for targeted reinforcement.