Tunnelling processes lead to stress changes surrounding an underground opening resulting in the disturbance and potential damage of the surrounding ground. Especially, when it comes to hard rocks at great depths, the rockmass is more likely to respond in a brittle manner during the excavation. Continuum numerical modelling and discontinuum techniques have been employed in order to capture the complex nature of fracture initiation and propagation at low‐confinement conditions surrounding an underground opening. In the present study, the hybrid finite‐discrete element method (FDEM) is used and compared to techniques using the finite element method (FEM), in order to investigate the efficiency of these methods in simulating brittle fracturing. The numerical models are calibrated based on data and observations from the Underground Research Laboratory (URL) Test Tunnel, located in Manitoba, Canada. Following the comparison of these models, additional analyses are performed by integrating discrete fracture network (DFN) geometries in order to examine the effect of the explicit simulation of joints in brittle rockmasses. The results show that in both cases, the FDEM method is more capable of capturing the highly damaged zone (HDZ) and the excavation damaged zone (EDZ) compared to results of continuum numerical techniques in such excavations.

Rockfall constitutes a major threat to the safety and sustainability of transport corridors bordered by rocky cliffs. This research introduces a new approach to rockfall susceptibility modeling for the identification of potential rockfall source zones. This is achieved by developing a data-driven model to assess the local slope morphological attributes with respect to the rock slope evolution processes. The ability to address “where” a rockfall is more likely to occur via the analysis of historical event inventories with respect to terrain attributes and to define the probability of a given area producing a rockfall is a critical advance toward effective transport corridor management. The availability of high-quality digital volumetric change detection products permits new developments in rockfall assessment and prediction. We explore the potential of simulating the conceptualization of slope-scale rockfall susceptibility modeling using computer power and artificial intelligence (AI). We employ advanced 3D computer vision algorithms for analyzing point clouds to interpret high-resolution digital observations capturing the rock slope evolution via long-term, LiDAR-based 3D differencing. The approach has been developed and tested on data from three rock slopes: two in Canada and one in the UK. The results indicate clear potential for AI advances to develop local susceptibility indicators from local geometry and learning from recent rockfall activity. The resultant models produce slope-wide rockfall susceptibility maps in high resolution, producing up to 75% agreement with validated occurrences.

Reality capture technologies, also known as close-range sensing, have been increasingly popular within the field of engineering geology and particularly rock slope management. Such technologies provide accurate and high-resolution n-dimensional spatial representations of our physical world, known as 3D point clouds, that are mainly used for visualization and monitoring purposes. To extract knowledge from point clouds and inform decision-making within rock slope management systems, semantic injection through automated processes is necessary. In this paper, we propose a model that utilizes a segmentation procedure which delivers segments ready to classify and be retained or rejected according to complementary knowledge-based filter criteria. First, we provide relevant voxel-based features based on the local dimensionality, orientation, and topology and partition them in an assembly of homogenous segments. Subsequently, we build a decision tree that utilizes geometrical, topological, and contextual information and enables the classification of a multi-hazard railway rock slope section in British Columbia, Canada into classes involved in landslide risk management. Finally, the approach is compared to machine learning integrating recent featuring strategies for rock slope classification with limited training data (which is usually the case). This alternative to machine learning semantic segmentation approaches reduces substantially the model size and complexity and provides an adaptable framework for tailored decision-making systems leveraging rock slope semantics.