Computational-Rock Mechanics in Pedagogy and Practice

ABSTRACT: Point cloud modeling of rock slopes using LIDAR and Structure-from-Motion digital stereophotogrammetry provides, at a minimum, thousands of facets and facet normals that can be used to identify the densities of orientations of rock mass discontinuities, the geometries of potentially removable blocks, and the character of the excavation face. As part of the Engineering Geology graduate curriculum at the Civil and Environmental Engineering program at the University of California, Berkeley we teach graduate students an integrated methodology for a gathering point cloud information be laser or camera; b computing facets and facet normals form point clouds for stereonet presentation and geometric analysis of block dimension; c extract rock mass discontinuities from stereonet data to analyze key blocks, assess discontinuous deformation analysis (DDA) behavior, and model rock slope stability. These new methods require a suite of different software tools discussed in the paper to move through the workflow process. Computational rock mechanics provides data sets that are orders of magnitude richer in detail and result in better understanding of rock slope and tunnel key block behavior. Full application of computational rock mechanics methods should reduce the cost of bolting by identifying critical support orientations and design loads. 1. INTRODUCTION This paper is intended to serve as an instruction manual for teachers who include digital methods in rock mechanics. Innovations in digital methods for computing surface models have advanced such that models can be used for quantitative rock mechanics analyses. An array of new remote sensing tools utilizes laser and photogrammetric means to build digital twins of a surface at millimeter-to-centimeter accuracy by processing a point cloud of the target. The oldest of these technologies, Terrestrial Laser Scanning (TLS), has transformed the ability of engineers to document geotechnical sites. Though TLS remains an expensive technique for capturing point clouds of data needed to model surfaces, the cost has dropped in the last decade, and is now widely available on common cell phones. A remarkable new computer vision-based method, Structure-from-Motion (SfM), allows engineers to build complex pointcloud visualizations with digital cameras (airborne or handheld) at a fraction of the cost. In the sections below, we outline a procedure for gathering data and moving through the analysis process to extract meaningful rock mechanics information from point cloud data. The procedural steps are outlined in Figure 1.