Abstract
Machine learning methods for data processing are gaining momentum in many geoscience industries. This includes the mining industry, where machine learning is primarily being applied to autonomously driven vehicles such as haul trucks, and ore body and resource delineation. However, the development of machine learning applications in rock engineering literature is relatively recent, despite being widely used and generally accepted for decades in other risk assessment-type design areas, such as flood forecasting. Operating mines and underground infrastructure projects collect more instrumentation data than ever before, however, only a small fraction of the useful information is typically extracted for rock engineering design, and there is often insufficient time to investigate complex rock mass phenomena in detail. This paper presents a summary of current practice in rock engineering design, as well as a review of literature and methods at the intersection of machine learning and rock engineering. It identifies gaps, such as standards for architecture, input selection and performance metrics, and areas for future work. These gaps present an opportunity to define a framework for integrating machine learning into conventional rock engineering design methodologies to make them more rigorous and reliable in predicting probable underlying physical mechanics and phenomenon.
Highlights
The study of rock mechanics encompasses the theoretical and applied science of the mechanical behaviour of rock in response to its physical environment and was formalized as a field of study in the 1960s [1]
This paper aims to summarize the most common practices in rock engineering design and presents the opportunities for integrating machine learning into existing geomechanical design frameworks
While other numerical predictors are briefly discussed, this paper focuses on Artificial Neural Networks (ANNs) as a prediction tool, as they have been the focus of the research performed in machine learning methods for rock engineering design
Summary
The study of rock mechanics encompasses the theoretical and applied science of the mechanical behaviour of rock in response to its physical environment and was formalized as a field of study in the 1960s [1]. Experience and expert knowledge are heavily relied upon in rock engineering practice and empirical design charts have become prolific for preliminary stage design. Numerical modelling methods such as continuum and discrete methods, are conventional tools in rock engineering design. These methods represent important tools for understanding rock mass behaviour and predicting its response to its environment and changes in in situ stress conditions. It is often difficult to integrate all the data collected into empirical and numerical models effectively due to time and budget constraints, as well as limitations in how constitutive behaviour is defined. It is sometimes not feasible to collect the quality and quantity of data needed, so extrapolation and interpolation techniques are often used
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