scholarly journals Estimating Deformation Modulus of Rock Mass from Beniawski's Rock Mass Rating(RMR).

2000 ◽  
pp. 283-286
Author(s):  
Yukihiko OKABE ◽  
Masato SHINJI ◽  
Xu WU ◽  
Toshikazu KAWAMOTO
Author(s):  
Jianye Ching ◽  
Kok-Kwang Phoon ◽  
Yuan-Hsun Ho ◽  
Meng-Chia Weng

A generic rock mass database consisting of 9 parameters is compiled from 225 studies. The 9 parameters include the deformation modulus, elastic modulus, dynamic modulus, rock quality designation, rock mass rating, Q-system, geological strength index of a rock mass as well as intact-rock Young’s modulus and intact-rock uniaxial compressive strength. This generic database, labeled as ROCKMass/9/5876, consists of 5876 rock mass cases. The goal of this paper is to examine how an existing transformation model such as deformation modulus versus rock mass rating can be made more unbiased and more precise for a specific site by combining sparse site data with ROCKMass/9/5876 in a manner sensitive to site-specific differences. The outcome is a quasi-site-specific transformation model. Four methods are studied to construct a quasi-site-specific transformation model for the deformation modulus of a rock mass: probabilistic multiple regression (current state of practice), hybridization method, hierarchical Bayesian model, and similarity method. The results from two case studies in Turkey show that the hierarchical Bayesian model is the most effective.


2010 ◽  
Vol 33 (2) ◽  
pp. 153-158
Author(s):  
Mohamed Hassan Aboud ◽  
Mohamed Ahmad Osman

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Satar Mahdevari ◽  
Mohammad Hayati

AbstractDesigning a suitable support system is of great importance in longwall mining to ensure the safe and stable working conditions over the entire life of the mine. In high-speed mechanized longwall mining, the most vulnerable zones to failure are roof strata in the vicinity of the tailgate roadway and T-junctions. Severe roof displacements are occurred in the tailgate roadway due to the high-stress concentrations around the exposed roof span. In this respect, Response Surface Methodology (RSM) was utilized to optimize tailgate support systems in the Tabas longwall coal mine, northeast of Iran. The nine geomechanical parameters were obtained through the field and laboratory studies including density, uniaxial compressive strength, angle of internal friction, cohesion, shear strength, tensile strength, Young’s modulus, slake durability index, and rock mass rating. A design of experiment was developed through considering a Central Composite Design (CCD) on the independent variables. The 149 experiments are resulted based on the output of CCD, and were introduced to a software package of finite difference numerical method to calculate the maximum roof displacements (dmax) in each experiment as the response of design. Therefore, the geomechanical variables are merged and consolidated into a modified quadratic equation for prediction of the dmax. The proposed model was executed in four approaches of linear, two-factor interaction, quadratic, and cubic. The best squared correlation coefficient was obtained as 0.96. The prediction capability of the model was examined by testing on some unseen real data that were monitored at the mine. The proposed model appears to give a high goodness of fit with the accuracy of 0.90. These results indicate the accuracy and reliability of the developed model, which may be considered as a reliable tool for optimizing or redesigning the support systems in longwall tailgates. Analysis of variance (ANOVA) was performed to identify the key variables affecting the dmax, and to recognize their pairwise interaction effects. The key parameters influencing the dmax are respectively found to be slake durability index, Young’s modulus, uniaxial compressive strength, and rock mass rating.


2019 ◽  
Vol 9 (10) ◽  
pp. 2065 ◽  
Author(s):  
Jonguk Kim ◽  
Hafeezur Rehman ◽  
Wahid Ali ◽  
Abdul Muntaqim Naji ◽  
Hankyu Yoo

In extensively used empirical rock-mass classification systems, the rock-mass rating (RMR) and tunneling quality index (Q) system, rock-mass quality, and tunnel span are used for the selection of rock bolt length and spacing and shotcrete thickness. In both systems, the rock bolt spacing and shotcrete thickness selection are based on the same principle, which is used for the back-calculation of the rock-mass quality. For back-calculation, there is no criterion for the selection of rock-bolt-spacing-based rock-mass quality weightage and shotcrete thickness along with tunnel-span-based rock-mass quality weightage. To determine this weightage effect during the back-calculation, five weightage cases are selected, explained through example, and applied using published data. In the RMR system, the weightage effect is expressed in terms of the difference between the calculated and back-calculated rock-mass quality in the two versions of RMR. In the Q system, the weightage effect is presented in plots of stress reduction factor versus relative block size. The results show that the weightage effect during back-calculation not only depends on the difference in rock-bolt-spacing-based rock-mass quality and shotcrete along with tunnel-span-based rock-mass quality, but also on their corresponding values.


2010 ◽  
Vol 25 (4) ◽  
pp. 333-345 ◽  
Author(s):  
Jafar Khademi Hamidi ◽  
Kourosh Shahriar ◽  
Bahram Rezai ◽  
Jamal Rostami

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