Evaluation of effect of rock mass properties on fragmentation using robust techniques

In the civil and mining projects, blasting operation is important from technical and economical point of view. There are several parameters which affect the result of operation such as desired fragmentation and undesired phenomena, e.g., ground vibration, fly rock, etc. From these parameters, rock mass characterizations can be considered as more influential as compared to the blasting pattern. In other words, it can be said that pattern specifications should primarily be designed according to the rock mass properties to reach the main objective of the operation, i.e., rock fragmentation. Complex nature of the problem needs to implement robust approaches such as artificial intelligence-based techniques. In this paper, an attempt has been made to develop some models by which the impact of each and every parameter influencing the result of blasting operation can be evaluated. For this research work, 432 datasets from 14 mines situated in the different parts of the world has been collected. In developing of the models, 19 parameters such as uniaxial compressive strength, tensile strength, brittleness, Point Load Index, Young’s modulus, Poisson’s ratio, rock quality designation, cohesion, friction angle, burden, spacing and stemming were incorporated. Regression analysis, decision tree and artificial neural network methods were employed for developing the models for predicting fragmentation. Determination coefficient (R²) for artificial neural network modeling, multivariate linear regression and decision tree was computed 0.98, 0.83 and 0.45, respectively, showing accuracy of network modeling over the other applied methods. In addition, it was revealed that the most influential parameters on fragmentation are Point Load Index, uniaxial compressive strength, Poisson’s ratio, cohesion and rock quality designation, respectively, and the least effective ones are stemming, spacing and hole diameter, respectively.