Existing data acquisition techniques for structural data collection are manual, time-consuming, and can expose technical manpower to hazardous conditions. Advances in Unmanned Aerial Vehicles (UAVs) technology allows collecting photogrammetry data of pit slopes. The aerial approach is fast, on demand and can improve the spatial and temporal resolution of the collected structural data. The collected data can be used to generate digital elevation models (DEMs) and point clouds to assess the slope configuration and to perform virtual mapping of the pit wall to collect detailed structural data.

This study presents the application of UAV technology to collect structural data from a pit wall, in Nevada, USA. The aerial photogrammetry method is used to generate a point cloud of the pit slope for virtual structural mapping. The structural mapping data is then integrated into the surveyed pit slope geometry to generate a conditioned Discrete Fracture Network (DFN) model. The discontinuities mapped on the slope surface are replicated in the DFN model, while behind the pit wall, a constrained stochastic model is used to describe the structural complexity of the rock mass. This combined stochastic-deterministic DFN model is used to conduct a kinematic stability analysis of the pit slope. The results are compared to the field observations of pit slope failure.

1. INTRODUCTION

Knowledge of rock mass structural characteristics is necessary for most geotechnical engineering analyses. The information required includes the geometrical description of discontinuities present in the rock mass such as their orientation, persistence, size, intensity, roughness, and general condition. Kinematic analysis of pit slopes and in-situ block size analysis are examples of such analyses that rely on detailed rock mass structural characteristics. Geotechnical engineers have different tools at their disposal for doing these types of analyses. This includes using discrete fracture networks (DFNs) modelling, limit equilibrium programs, and empirical relationships. The selection of the appropriate tool depends on the type and quality of data available, and the desired output. The reliability of these techniques depends on the accuracy of the input data. Moreover, the outputs need to be verified and calibrated, which also requires the collection of accurate follow-up data.

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