![]() Thematic collection: This article is part of the Digitization and Digitalization in engineering geology and hydrogeology collection available at: 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. ![]() 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. There is an empirical knowledge gap that cannot be bridged by the use of technology alone. 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. Moreover, the relationship between the geometrical and mechanical RVE sizes is also established.ĭespite 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. Finally, the geometrical RVE size (10 m) and mechanical RVE size (18 m) are determined with the coefficient of variation. ![]() Subsequently, on the basis of the sampling methods considering the special natures, the special natures of the geometrical and mechanical parameters are studied in detail and fully considered to improve the accuracy of the RVE results. Therefore, the traditional method of RVE determination needs to be improved. Through the comparison and analysis of the RVEs in different regions and directions, it is discovered that the inhomogeneity and anisotropy of the rock result in the spatial effect and directional effect in the RVE size, respectively. The representative parameters for RVE determination are selected and presented first. 3D fracture networks are generated on the basis of fracture data in the field and then used in this study for RVE determination. This study takes the rock masses in the dam foundation of a sluice gate of the Datengxia Hydropower Station in China as a case study to determine the geometrical and mechanical representative volume elements (RVEs) considering the special natures of rock masses (inhomogeneity and anisotropy).
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