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.     

3D random Voronoi grain-based models for simulation of brittle rock damage and fabric-guided micro-fracturing

A grain-based distinct element model featuring three-dimensional （3D） Voronoi tessellations （randompoly-crystals） is proposed for simulation of crack damage development in brittle rocks. The grainboundaries in poly-crystal structure produced by Voronoi tessellations can represent flaws in intact rockand allow for numerical replication of crack damage progression through initiation and propagation ofmicro-fractures along grain boundaries. The Voronoi modelling scheme has been used widely in the pastfor brittle fracture simulation of rock materials. However the difficulty of generating 3D Voronoi modelshas limited its application to two-dimensional （2D） codes. The proposed approach is implemented inNeper, an open-source engine for generation of 3D Voronoi grains, to generate block geometry files thatcan be read directly into 3DEC. A series of Unconfined Compressive Strength （UCS） tests are simulated in3DEC to verify the proposed methodology for 3D simulation of brittle fractures and to investigate therelationship between each micro-parameter and the model＇s macro-response. The possibility of numericalreplication of the classical U-shape strength curve for anisotropic rocks is also investigated innumerical UCS tests by using complex-shaped （elongated） grains that are cemented to one another alongtheir adjoining sides. A micro-parameter calibration procedure is established for 3D Voronoi models foraccurate replication of the mechanical behaviour of isotropic and anisotropic （containing a fabric） rocks.
2014 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.     

Prediction of rock fragmentation due to blasting using artificial neural network

Prediction of rock fragmentation is essential for optimizing blasting operation. Fragmentation depends on many parameters such as rock mass properties, blast geometry and explosive properties. In this paper, artificial neural network (ANN) method is implemented to develop a model to predict rock fragmentation due to blasting in an iron ore mine. In the developing of the proposed model eight parameters such as hole diameter, burden, powder factor, blastability index, etc., were incorporated. Training of the model was performed by back-propagation algorithm using 220 datasets. A four-layer ANN was found to be optimum with architecture 10-9-7-1. Sensitivity analysis revealed that the most effective parameters on rock fragmentation are blastability index (G), charge per delay (J), burden (C), SMR (F) and powder factor (E).     

Comprehensive compressive-tensile strength criterion for intact rock

This paper presents a strength criterion for intact rock,which can well describe triaxial test data under compressive or tensile stress state.The proposed criterion is defined in terms of three parameters.One parameter expresses the apparent unconfined compressive strength(AUCS),obtained from the Coulomb-Mohr criterion,as a regulated unconfined compressive strength(RUCS).Two other parameters,and,are material-dependent that can be determined by regression analysis.The proposed criterion is compared with selected applicable strength criteria separately for compressive and tensile strengths.Coefficient of determination and accordance coefficient are considered in comparisons between the proposed and selected strength criteria.