A Review on Machine Learning approaches for Himalayan Rock Mass Classification

Abstract

Abstract Among all of the subsystems and components of hydropower, the tunnel is one of the crucial structures that face most of the pre-operation, real-time and post-operation challenges. Tunnels used as a water conveyance system in the majority of the projects lead to significant importance in the hydropower sector. The engineering rock mass classification is a crucial step in the accomplishment of underground constructions, particularly tunnels and caverns within the rock mass. The quality of rock mass parameters must be assessed and predicted with greater accuracy. However, the difficulties lie in the proper assessment of these parameters to define their quality. Studies have shown that despite the use of numerical and empirical methods in construction practices, numerous problems are reported when it comes to dealing with such structures. Over the years, various approaches based on machine learning (ML) techniques have been used for the minimization of these difficulties. As in recent trends, the development of machine learning technologies is currently bringing new methodologies in assessing rock mass classification. Although, because a sufficiently large database is required for such initiatives, there are a number of difficulties in their practical execution. Thus, a review of studies on the use of machine learning techniques in rock mass classification is presented in this paper. To determine the future directions of ML in RMC, it presents a comprehensive assessment to examine how ML models are being used. This study offers recommendations for creating an industry-specific model focusing on the strengths and weaknesses of ML techniques