Smart Observational Methods for Geomechanical Systems

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Risk is an unavoidable component of engineered systems and is particularly relevant to structures involving soil and rock. These materials are notoriously heterogeneous at all scales - from micrometer-scale strength contrasts at mineral grain contacts, all the way up to kilometer-scale earthquake fault zones. Geostructures face additional external risk factors such as climate change and earthquake damage that must be considered. Some of these uncertainties can be managed with consistent monitoring to detect and characterise changes in performance. However, existing long-term monitoring strategies can be costly and limited in temporal and spatial resolution. The long-term goal of the research program is to develop cost-effective and high-resolution tools for the characterisation and monitoring of various geostructures. The research program will achieve broad spatial coverage by employing stationary and aerial imaging in conjunction with three-dimensional digital image correlation to 1) calibrate for material and geometric properties of slopes, and 2) develop supervised machine learning methods to process these image data and predict slope instability. These tools will first be developed on scale models of slopes in Western University's state-of-the-art drum centrifuge, and then validated and updated on drone images of a quarry in southern Ontario. The resulting methodologies are expected to assist civil and mining engineers in characterising slopes over large areas and identify highways, dams, natural resources, and buildings in proximity to progressive landslides. The research program will achieve high temporal resolution monitoring by employing acoustic emission sensors, which are sensitive to high frequency elastic waves, to detect, locate, and classify micrometer-scale shear and tensile cracks. These micro-cracks have short durations and thus cannot be detected with conventional load, strain, or pore-pressure transducers which are only sensitive to lower frequency perturbations. We will 3) develop machine learning methods to identify and classify changes in fluid-soil-rock systems by analysing acoustic emission signal characteristics during ring-shear experiments, and 4) investigate discrete acoustic emission events originating from surface ruptures of earthquakes in scaled centrifuge models. These acoustic emission tools can guide civil and energy resource engineers in diagnosing problematic cracks and leaks in infrastructure projects such as hydroelectric dams or underground nuclear waste storage, and optimise flow networks in geothermal systems. The experiments will also advance our fundamental science understanding of earthquake faulting and cracking mechanisms. Our fluid-soil-rock experiments in the ring shear device will shed light on the frequency characteristics of fluid-rich fault zones, and our centrifuge experiments will reveal details of stress localisation when large earthquakes propagate to the ground surface.