Is there a Universal Rock Mass Classification System?

This paper examines the consequences of subjectivity on universal design methodologies, and emphasizes the need for a cohesive and globally accepted rock mass classification (RMC) framework to foster innovation and efficiency in rock engineering projects. Challenges posed by varied rock mass classification systems and core logging practices complicate the task of data collection and preparation for emerging technologies. Integrating new technologies into rock engineering relies heavily on quality, standardized data. Inconsistencies in terminology, measurement standards, and emphasis on specific rock characteristics can introduce biases which will hinder the development of robust designs. This paper examines the importance of overcoming data preparation hurdles in order to utilize the full potential of new technologies in rock engineering. By unravelling the intricacies of this multidimensional problem, this study provides a foundation for discussions on standardization, collaboration, and innovation within the field, ultimately paving the way for more reliable and efficient rock engineering practices on a global scale. This research aims to provide insights that will contribute to the overarching goal of developing universal design methods for rock engineering while addressing the current challenges of data preparation in rock engineering. 1. INTRODUCTION In the past few years, the field of Artificial Intelligence has flourished. Numerous, unrelated industries have adopted machine learning to improve workflow, worker safety and productivity (Patel et al., 2021). Interest in artificial intelligence models for applications in the Rock Engineering field has increased significantly in recent years. Artificial Intelligence (A.I.) was defined by John McCarthy (2004) as "the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable". To this end, various models have been developed and deployed for Rock Engineering applications such as using Convoluted Neural Networks (C.N.N.) for optical rock fracture mapping (Azhari et al, 2021).