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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]