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
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.