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Over the past decades, numerical modelling has become a powerful tool for rock mechanics applications. However, the accurate estimation of rock mass input parameters remains a significant challenge. Machine learning (ML) tools have recently been integrated to enhance and accelerate numerical modelling processes. In this paper, we demonstrate the novel use of ML tools for calibrating a state-of-the-art three-dimensional (3D) finite-element (FE) model of a kinematic structurally controlled failure event in an open-pit mine. The failure event involves the detachment of a large wedge, thus allowing for the accurate identification of the geometry of the rock joints. FE models are automatically generated according to estimated ranges of joint input parameters. Subsequently, ML tools are used to analyze the synthetic data and calibrate the strength parameters of the rock joints. Our findings reveal that a relatively small number of models are needed for this purpose, rendering ML a highly useful tool even for computationally demanding FE models. [ABSTRACT FROM AUTHOR]

In their highly influential paper, Yarkoni, Tal, and Jacob Westfall. 2017. "Choosing Prediction over Explanation in Psychology: Lessons from Machine Learning." Perspectives on Psychological Science 12 (6):1100–1122. the authors highlight difficulties in traditional explanatory research in the field of psychology and argue in favour of novel data-driven science. By applying machine-learning methods to large data sets, predictive power has been shown to increase significantly. Geological engineers are responsible for a wide range of applications, including the design of tunnels, dams, foundations, and mines. While the field of geological engineering stands on solid mechanistic grounds, we argue that its predictive aspect aligns more closely with psychology than other mechanistic sciences. We therefore propose a paradigm shift in geological engineering research towards a prediction-centric approach. Potentially, this could enhance cost-effectiveness in structural design and lead to substantial societal savings. [ABSTRACT FROM AUTHOR]

This paper presents modeling of rock spalling induced by a dynamic pulse using the finite-discrete element method, for the purpose of tunnel support design to withstand blasting. 1D and 2D model results are compared to analytical spalling equations and field test findings. It was found that only the 2D models are suitable for support design. A distinction between heavy spalling and light rockfall is made based on an estimation of the ratio of the peak stress of the arriving wave to the rock tensile strength. Accordingly, different design approaches are suggested: for heavy spalling a low impedance isolating layer between the tunnel liner and surrounding rock is recommended. For light rockfall, a simplified static FEM analysis procedure is presented. [ABSTRACT FROM AUTHOR]