1.Department. of Civil Engineering, Shandong University of Science and Technology, Qingdao 266590, China
2.College of Civil Engineering, Fujian University of Technology, Fuzhou, 350118, China
Abstract The rock mass rating (RMR) system is one of the most commonly used methods for classifying rock masses in underground engineering. Uncertainty of RMR values can significantly affect the safety of underground projects. In this regard, we proposed a reliable rating approach for classifying rock masses based on the reliability theory. This theory was incorporated into the RMR system to establish the functions of rock masses of different classifications. By analyzing the probability distribution patterns of various parameters used in the RMR system and using the Monte Carlo method to calculate the reliability probability of surrounding rock belonging to each
classification, reliable RMR values for the rock mass to be excavated can be obtained. The results demonstrate that it is feasible to adopt the reliability theory in classification tasks considering the randomness characteristics of rock and soil. As verified through a case study of the Lushan Tunnel project, the proposed approach can be used to obtain the probability of the uncertainty of the calculated RMR values of underground engineering rock masses, and the calculation results are consistent with reality. The proposed approach can serve as a reference for studies in other fi elds and also applies to other rock mass classifi cation methods.
Fund: This work was supported by the National Natural Science Foundation of China [Grant No. 52079077] and China Postdoctoral Science Foundation (Grant No. 2022M711962)
About author: Dr. Peng He was born in Laiwu, Shandong Province,in 1988. He is currently an Associate Professor mainly engaged in the research of stability analysis and dynamic evaluation and control of tunnel rock mass structure.
Cite this article:
. Quick and reliable approach for rating underground engineering rock mass based on RMR system[J]. APPLIED GEOPHYSICS, 2025, 22(2): 447-460.
2025, Vol. 22 
