Affiliations 

  • 1 Department of Geology (Centre for Advanced Studies), University of Delhi, Delhi, 110007, India. md.azaddu@gmail.com
  • 2 Operation and Maintenance, Operation, Maintenance and Acoustics, Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Lulea, Sweden. taoufik.najeh@ltu.se
  • 3 CSIR-Central Institute of Mining and Fuel Research (Ministry of Science & Technology, Govt. of India), Nagpur Research Center 17/C, Telangkhedi Area, Civil Lines, Nagpur, Maharashtra, 440001, India
  • 4 Earthquake Monitoring Center, Sultan Qaboos University, Muscat, 123, Oman
  • 5 Department of Transport Systems, Traffic Engineering and Logistics, Silesian University of Technology, Krasińskiego 8 Street, Katowice, Poland
  • 6 Department of Geosciences, Geotechnology and Materials Engineering for Resources, Graduate School of International Resource Sciences, Akita University, Akita, 010-8502, Japan. yowagaye@gmail.com
  • 7 Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia. yaser.g@monash.edu
  • 8 Department of Geology (Centre for Advanced Studies), University of Delhi, Delhi, 110007, India
Sci Rep, 2024 May 10;14(1):10716.
PMID: 38729957 DOI: 10.1038/s41598-024-60289-y

Abstract

Engineering rockmass classifications are an integral part of design, support and excavation procedures of tunnels, mines, and other underground structures. These classifications are directly linked to ground reaction and support requirements. Various classification systems are in practice and are still evolving. As different classifications serve different purposes, it is imperative to establish inter-correlatability between them. The rating systems and engineering judgements influence the assignment of ratings owing to cognition. To understand the existing correlation between different classification systems, the existing correlations were evaluated with the help of data of 34 locations along a 618-m-long railway tunnel in the Garhwal Himalaya of India and new correlations were developed between different rock classifications. The analysis indicates that certain correlations, such as RMR-Q, RMR-RMi, RMi-Q, and RSR-Q, are comparable to the previously established relationships, while others, such as RSR-RMR, RCR-Qn, and GSI-RMR, show weak correlations. These deviations in published correlations may be due to individual parameters of estimation or measurement errors. Further, incompatible classification systems exhibited low correlations. Thus, the study highlights a need to revisit existing correlations, particularly for rockmass conditions that are extremely complex, and the predictability of existing correlations exhibit high variations. In addition to augmenting the existing database, new correlations for metamorphic rocks in the Himalayan region have been developed and presented that can serve as a guide for future rock engineering projects in such formations and aid in developing appropriate excavation and rock support methodologies.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.