Non-Contact Rock Strength Characterization

Abstract

Understanding and classifying the compressive strength of rock, particularly the Unconfined Compressive Strength (UCS), is fundamental to rock mass classification and geotechnical design in mining, tunneling, and civil infrastructure development. Traditional methods rely on index testing and destructive laboratory testing, often requiring engineers and geologists to be exposed to rockfall on steep slopes, and producing sparse data sets due to sampling, access, and cost limitations. This dissertation investigates a novel non-contact methodology to characterize intact rock strength using image-based technologies across the electromagnetic spectrum, specifically long-wave infrared and short-wave infrared. The research was conducted in two phases: (1) a field-deployed LWIR imaging study to detect rockfall events using thermal infrared cameras; and (2) the Multi-Image Deformation Analysis System (MIDAS), which used SWIR hyperspectral imaging and machine learning to classify UCS categories from an altered porphyry granite in Arizona. The result demonstrated that thermal imaging can detect rockfall under a range of environmental conditions, from the extreme cold of winter in British Columbia to summer heat at mines in Arizona. In the laboratory, several SWIR absorption features could be correlated to the UCS strength of the granite, and a k-nearest neighbors classification could be used to classify rock strength according to the ISRM classification. While there are limits in detecting quartz and feldspar in the SWIR, the study highlights the potential for expanded spectral coverage and integration into geotechnical workflows. Additionally, a path is outlined for developing physical rock descriptions for engineering classifications using the VNIR and SWIR spectroscopy. This dissertation contributes a repeatable, non-destructive, and auditable framework for strength classification that improves safety, enhances data coverage, and supports the utilization of commercial off-the-shelf imaging technologies from exploration to mining applications. The approach has broad implications for safety and more effective site characterization in mining, civil tunneling, and critical infrastructure monitoring, particularly in altered rock masses.