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Geomechanics classifications are used to perform a preliminary assessment of rock slope stability for different purposes in civil and mining engineering. Among all existing rock mass classifications, slope mass rating (SMR) is one of the most commonly used for slopes. Although SMR is a geomechanics classification applied worldwide, often some misapprehensions and inaccuracies are made when professionally and scientifically used. Nearly all these miscalculations involve the influence of slope geometry and the dip and direction of the discontinuities. These problems can be overcome by a systematic assessment of SMR, which allows users to understand and visualize the relative orientation between discontinuities and slope. To fulfil this purpose, a complete and detailed definition of the angular relationships between discontinuities and slope are included in this paper, clarifying the assessment of the SMR parameters. Additionally, a Matlab-based open-source software for SMR (SMRTool) calculation is presented, avoiding miscalculations by automating the calculations and showing the graphical representation of slope and discontinuities. Finally, a general explanation of the method for the use of SMR is reviewed, stressing the common source of errors when applying this classification. The performance, benefits and usefulness of SMRTool are also illustrated in this paper through a specific case study. [ABSTRACT FROM AUTHOR]

Rock mass classification systems are widely used tools for assessing the stability of rock slopes. Their calculation requires the prior quantification of several parameters during conventional fieldwork campaigns, such as the orientation of the discontinuity sets, the main properties of the existing discontinuities and the geo-mechanical characterization of the intact rock mass, which can be time-consuming and an often risky task. Conversely, the use of relatively new remote sensing data for modelling the rock mass surface by means of 3D point clouds is changing the current investigation strategies in different rock slope engineering applications. In this paper, the main practical issues affecting the application of Slope Mass Rating (SMR) for the characterization of rock slopes from 3D point clouds are reviewed, using three case studies from an end-user point of view. To this end, the SMR adjustment factors, which were calculated from different sources of information and processes, using the different softwares, are compared with those calculated using conventional fieldwork data. In the presented analysis, special attention is paid to the differences between the SMR indexes derived from the 3D point cloud and conventional field work approaches, the main factors that determine the quality of the data and some recognized practical issues. Finally, the reliability of Slope Mass Rating for the characterization of rocky slopes is highlighted. [ABSTRACT FROM AUTHOR]

Abstract: Rock mass characterization requires a deep geometric understanding of the discontinuity sets affecting rock exposures. Recent advances in Light Detection and Ranging (LiDAR) instrumentation currently allow quick and accurate 3D data acquisition, yielding on the development of new methodologies for the automatic characterization of rock mass discontinuities. This paper presents a methodology for the identification and analysis of flat surfaces outcropping in a rocky slope using the 3D data obtained with LiDAR. This method identifies and defines the algebraic equations of the different planes of the rock slope surface by applying an analysis based on a neighbouring points coplanarity test, finding principal orientations by Kernel Density Estimation and identifying clusters by the Density-Based Scan Algorithm with Noise. Different sources of information – synthetic and 3D scanned data – were employed, performing a complete sensitivity analysis of the parameters in order to identify the optimal value of the variables of the proposed method. In addition, raw source files and obtained results are freely provided in order to allow to a more straightforward method comparison aiming to a more reproducible research. [Copyright &y& Elsevier]