Optimization of rock mass rating (RMR) classification for jointed rocks for underground structures
Underground structures, as the most important structures of our country, have a direct relationship with the national security and economy power of the country, and their design, implementation and maintenance are very necessary. Conducting the experimental classification of the rock mass is a very useful tool in evaluating the quality of the rock mass and designing the reinforcements, and also, it provides a good view of the behavior of the rock in the face of the tunnel. Classification of rock mass rating (RMR) is one of the most important and efficient methods of rock mass classification, which has been used in the past until today, but it has some shortcomings in the correct estimation of rock mass behavior. In this research, 10 sites of defense projects in different rock mass conditions, including weak and very jointed rocks, and suitable conditions have been examined. The defense projects have been analyzed experimentally, and also, numerically using Phase2 software. The experimental and numerical results of this research show that, in general, the use of rock mass classification can be very useful in evaluating the characteristics of rock mass. Moreover, the use of the RMR in rocks having good conditions correctly evaluate the rock mass and is considered as a suitable design basis. However, in weak rocks, there are flaws in the evaluation, and instead of the rock quality designation (RQD) parameters and discontinuity spacing, the jointing factor can be used. In this manner, the results are better compatible with the real conditions and numerical analysis. Furthermore, the results of the proposed reinforcements have been examined. These results show that the RMR classification works well for semi-stable rocks (RMR>50), but for weak rocks (RMR<50), it is better to analyze the rock mass by modified RMR, which considers jointing factor.
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Prediction of TBM performance in different rock types using input parameters of RMR by applying ML-based regression analysis
A. Dardashti, R. Ajalloeian *, J. Rostami, J. Hassanpour, A. Salimi
Tunneling&Underground Space Engineering, -
Study of land subsidence in Isfahan-Borkhar plain using radar images, preparation of subsidence potential map using a combination of methods of AHP and Fuzzy Logic and Multiple Linear Regression
b *, Mohsen Rafiee, Maryam Dehghani, Masoud Mahmoudpour
Journal of Iranian Association of Engineering Geology,