Rock Mass Rating Classification System Research Articles

Determination of sublevel stoping layout using a network flow algorithm and the MRMR classification system

Reduced access to shallow mineral resources, increased demand for minerals, and advances in technology have led to the development of deep underground mines. The location of the stopes and, finally, the determination of the mine layout is one of the problems in the design of any underground mine. In this study, a network flow algorithm was used to optimize the underground mine layout where sublevel stoping was chosen as the mining method. Due to the geomechanical constraints, the empirical Mining Rock Mass Rating classification system was used to determine the maximum stable width of the mineable stopes in the mine layout of each mining level. The model was run on a copper deposit and optimized for one mining level. The results obtained from running the model showed that 40 mineable stopes with a total recovery of 97% could be achieved. For validity check, the results were compared with those obtained from the algorithm of maximum value neighborhood. Application of the proposed model into the example block model revealed approximately 7% more economic value for the network flow algorithm than the maximum value neighborhood algorithm.

The ARMR Classification System and the Modified Hoek-Brown Failure Criterion Compared to Directional Shear Strength Models for Anisotropic Rock Masses

The anisotropic rock mass rating classification system, ARMR, has been developed in conjunction with the Modified Hoek-Brown failure to deal with varying shear strength with respect to the orientation and degree of anisotropy within an anisotropic rock mass. Conventionally, ubiquitous-joint or directional shear strength models have assumed a general rock mass strength, typically estimated using the Hoek-Brown failure criterion, and applied a directional weakness in a given orientation depending on the anisotropic nature of the rock mass. Shear strength of the directional weakness is typically estimated using the Barton-Bandis failure criterion, or on occasion, the Mohr-Coulomb failure criteria. Directional shear strength models such as these often formed the basis of continuum models for slopes and underground excavations in anisotropic rock masses. This paper compares ARMR and the Modified Hoek-Brown failure criterion to the conventional directional shear strength models using a case study from Western Australia.

Assessment of Rock Mass Quality and Support Estimation along Headrace Tunnel of a Small Hydropower in District Mansehra, Khyber Pakhtunkhwa, Pakistan

The main purpose of this study is to classify the rock mass quality by using rock mass quality (Q) and Rock Mass Rating (RMR) systems along headrace tunnel of small hydropower in Mansehra District, Khyber Pakhtunkhwa. Geological field work was carried out to determine the orientation, spacing, aperture, roughness and alteration of discontinuities of rock mass. The quality of rock mass along the tunnel route is classified as good to very poor quality by Q system, while very good to very poor by RMR classification system. The relatively good rock conditions are acquired via RMR values that are attributed to ground water conditions, joint spacing, RQD and favorable orientation of discontinuities with respect to the tunnel drive. The petrographic studies revealed that study area is mainly comprised of five major geological rock units namely quartz mica schist (QMS), garnet mica schist (GMS), garnet bearing quartz mica schist (G-QMS), calcareous schist (CS), marble (M). The collected samples of quartz mica schist, marble and garnet bearing quartz mica schist are fine to medium grained, compact and are cross cut by few discontinuities having greater spacing. Therefore, these rocks have greater average RQD, Q values, RMR ratings as compared to garnet mica schist and calcareous schist.

Modification of rock mass rating system using soft computing techniques

Classification systems such as rock mass rating (RMR) are used to evaluate rock mass quality. This paper intended to evaluate RMR based on a fuzzy clustering algorithm to improve linguistic and empirical criteria for the RMR classification system. In the proposed algorithm, membership functions were first extracted for each RMR parameter based on the questionnaires filled out by experts. RMR clustering algorithm was determined by considering the percent importance of each parameter in the RMR classification system. In all implementation stages of the proposed algorithm, no empirical judgment was made in determining the classification classes in the RMR system. According to the obtained results, the proposed algorithm is a powerful tool to modify the rock mass rating system and can be generalized for future research.

Geomechanical characterization of volcanic rocks using empirical systems and data mining techniques

This paper tries to characterize volcanic rocks through the development and application of an empirical geomechanical system. Geotechnical information was collected from the samples from several Atlantic Ocean islands including Madeira, Azores and Canarias archipelagos. An empirical rock classification system termed as the volcanic rock system (VRS) is developed and presented in detail. Results using the VRS are compared with those obtained using the traditional rock mass rating (RMR) system. Data mining (DM) techniques are applied to a database of volcanic rock geomechanical information from the islands. Different algorithms were developed and consequently approaches were followed for predicting rock mass classes using the VRS and RMR classification systems. Finally, some conclusions are drawn with emphasis on the fact that a better performance was achieved using attributes from VRS.

On the use of the RMR system for estimation of rock mass deformation modulus

Estimation of rock mass deformation modulus is the subject of many studies in rock engineering research work. Although numerous predictive models have been developed for the estimation of the deformation modulus, they cannot be generalized for other sites because of inadequate accuracy. Furthermore, it is very valuable that the predictive models involve some accessible input parameters. The rock mass rating (RMR) is a well-known geomechanical parameter, which is usually determined to describe the quality of rock mass in rock engineering projects. In this study, five parameter ratings of the RMR classification system are used to predict the deformation modulus of rock mass in the abutment of the Gotvand earth dam. Statistical analysis and an artificial neural network are employed to present two new predictive models. Finally, probabilistic analysis is used to predict the rock mass deformation modulus, which overcomes the low accuracy caused by the inherent uncertainty in prediction. The results indicated that the parameter ratings used in the RMR classification system can predict the rock mass deformation modulus with a satisfactory correlation. However, the parameters don’t have the same influence on the rock mass deformability with the joint condition and the groundwater as the major and minor influencing parameters, respectively.

An assessment of total RMR classification system using unified simulation model based on artificial neural networks

Engineering design has great importance in the cost and safety of engineering structures. Rock mass rating (RMR) system has become a reliable and widespread pre-design system for its ease of use and variety in engineering applications such as tunnels, foundations, and slopes. In RMR system, six parameters are employed in classifying a rock mass: uniaxial compressive strength of intact rock material (UCS), rock quality designation (RQD), spacing of discontinuities (SD), condition of discontinuities (CD), condition of groundwater (CG), and orientation of discontinuities (OD). The ratings of the first three parameters UCS, RQD, and SD are determined via graphic readings where the last three parameters CD, CG, and OD are estimated by the tables that are composed of interval valued linguistic expressions. Because of these linguistic expresions, the estimated rating values of the last three become fuzzy especially when the related conditions are close to border of any two classes. In such cases, these fuzzy situations could lead up incorrect rock class estimations. In this study, an empirical database based on the linguistic expressions for CD, CG, and OD is developed for training Artificial Neural Network (ANN) classifiers. The results obtained from graphical readings and ANN classifiers are unified in a simulation model (USM). The data obtained from five different tunnels, which were excavated for derivation purpose, are used to evaluate classification results of conventional method and proposed model. Finally, it is noted that more accurate and realistic ratings are reached by means of proposed model.

The analysis of radial displacements occurring near the face of a circular opening in weak rock mass

The effect of opening size, rock properties and stress conditions on the radial displacements ahead and behind of the advancing face of a circular opening is one of the most important topics in civil and mining engineering. In this paper we develop a prediction system for radial displacements around the face of an opening by combining rock mass rating classification system, a three dimensional finite difference code and multiple regression modeling. Predictive equations and response surfaces for rock mass response are proposed. By using these equations and response surfaces, it is possible to assess radial displacements around the face and to construct the longitudinal deformation profile of a circular opening with different diameters excavated in weak rock mass under different stress conditions. The predictive equations were validated by means of statistical tests and analytical solution. Proposed response surfaces and equations can provide the basis of guiding the design and construction of underground openings.