Characterization of a fractured rock mass using geological strength index: A discrete fracture network approach

Rock mass contains siginificant heterogeneity due to the presence of structural discontinuities. The failure in the rock mass may cause a serious concern to road and rail transportation. Rock mass characterization is a first step towards preliminary investigation for road or tunnel excavations. Different field-based methods have been developed to characterize the rock mass conditions. The Geological Strength Index (GSI) is a simple and commonly adopted method with wide applicability in rock engineering. Traditional approaches are limited to 2D exposures for mapping purposes, but block formation or joint intersection is a 3D parameter. The advancement in computational techniques led to significant involvement of numerical modeling techniques such as those backed with discrete fracture network (DFN). The remote sensing techniques render the data with high precession po-tential not accessible with conventional methods. The stochastic DFNs generated based upon the statistical distribution of the input parameters can represent the natural fracture system in 3D. The developed synthetic fracture network can be used to examine the rock mass characteristics. This work addresses the incorporation of the Discrete Fracture Network (DFN) in the estimation of the Geological Strength Index (GSI) of the rock mass. The work compares the results of DFN generated using aggregate and disaggregate approaches in block size distribution (BSD) and rock quality designation (RQD) measurements for a fractured slope. The calculated BSD and RQD using DFN and field-estimated joint condition parameter are used to estimate GSI of the rock mass. A machine learning based python GUI tool was developed to find GSI from block volume and joint condition parameters. The prediction of GSI from input parameters using machine learning has led to systematically digitizing the standard GSI chart.