In this paper, the authors review recent applications of an integrated numerical modeling approach based on the analysis of the mechanical behavior of discrete systems. The numerical analysis includes both a more realistic representation of fracture networks and the simulation of rock mass behavior as a combination of failure through intact rock and displacement/rotation along predefined discontinuities. Selected examples are presented with respect to a variety of engineering problems, including shear testing, failure of hard-rock pillars, slope stability, and block/panel cave mining. The results clearly illustrate the importance of including natural jointing to better capture rock mass behavior in response to loading and unloading. Particular emphasis is given to modeling cave development and surface subsidence, and the proposed numerical method is shown to capture fully the complex rock mass response to caving associated with multi lift extraction. Whereas the use of relatively complex numerical...

Despite recent efforts, digitization in rock engineering still suffers from the difficulty in standardizing and statistically analysing databases that are created by a process of quantification of qualitative assessments. Indeed, neither digitization nor digitalization have to date been used to drive changes to the principles upon which, for example, the geotechnical data-collection process is founded, some of which have not changed in several decades. There is an empirical knowledge gap that cannot be bridged by the use of technology alone. In this context, this paper presents the results of what the authors call a rediscovery of rock mass classification systems, and a critical review of their definitions and limitations in helping engineers to integrate these methods and digital acquisition systems. This discussion has significant implications for the use of technology as a tool to directly determine rock mass classification ratings and for the application of machine learning to address rock engineering problems. Thematic collection: This article is part of the Digitization and Digitalization in engineering geology and hydrogeology collection available at: https://www.lyellcollection.org/cc/digitization-and-digitalization-in-engineering-geology-and-hydrogeology

In observational method projects in geotechnical engineering, the final geotechnical design is decided upon during actual construction, depending on the observed behavior of the ground. Hence, engineers must be prepared to make crucial decisions promptly, with few available guidelines. In this paper, we propose coupling numerical analysis with machine learning (ML) algorithms for enhancing the decision process in observational method projects. The proposed methodology consists of two main computational steps: (1) data generation, where multiple numerical models are automatically generated according to the anticipated range of input parameters, and (2) data analysis, where input parameters and model results are analyzed with ML models. Using the case study of the Semel tunnel in Tel Aviv, Israel, we demonstrate how this computational process can contribute to the success of observational method projects through (1) the computation of feature importance, which can assist with better identifying the key features that drive failure prior to project execution, (2) providing insights regarding the monitoring plan, as correlative relationships between various results can be tested, and (3) instantaneous predictions during construction.