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The blastability grading of a rock mass is an important parameter for the blasting design in metal mines. Appropriate blastability grading results guide the production to increase the efficiency and reduce the costs. In this paper, the multi-factors index system of the rock mass blastability consisting of the density, the p-wave velocity, the wave impedance, the uniaxial compressive strength, the rock elasticity, and the uniaxial tensile strength is determined for the Tongkeng ore in Guangxi, China. The determination of such indicator system is based on the multi-factors interaction, ambiguity, and randomness of the rock engineering system (RES). Based on the system engineering theory, a new improved RES-multidimensional cloud rock mass blastability classification model is established. The results show that the cloud-inference expert semi-quantitative (CESQ) can effectively obtain the expert experience and decrease the subjectivity of the traditional RES methods, and based on the improved fuzzy RES, the interaction factors among the factors and the systems affecting the blasting morphology of the rock mass are analyzed. Moreover, the importance of in situ testing of mechanical parameters of different rock masses is determined; finally, the five 6-D cloud models for the rock mass blastability grade are generated via the multidimensional cloud theory. The program is designed to support the engineering case analysis and is consistent with the results of the sample blasting experiment. A biased evaluation of the edge results is carried out, and this improved the actual usability of the evaluation model.

Improved Rock Engineering System (RES)- Multidimensional Cloud Evaluation Model and Its Application to the Rock Mass Blastability