Journal
INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES
Volume 160, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijrmms.2022.105250
Keywords
Risk assessment; Rock engineering system; Fuzzy cognitive map; Blasting operation; Rock mass fragmentation
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The study successfully predicted rock fragmentation and evaluated the risks using the RRECS model. Validation of the model showed its superior performance compared to other models. Sensitivity analysis revealed the importance of hole diameter and powder factor parameters on rock fragmentation.
Blasting is an important mining operation that usually produce several damaging consequences. Adverse rock fragmentation due to bench blasting is one of them. Hence, analysis of risk level and accurate estimation of particle size distribution of fragment size are of interest. This research developed a new model to simultaneously predict and risk assessment of rock fragmentation using 64 collected data from blasting operated in the Zarshouran gold mine in Iran. In this regard, a newly rock engineering system (RES) is developed based on the reliability information of Z-number theory and causal-effect relationship of the fuzzy cognitive map (FCM). This approach is named the reliability rock engineering causality system (RRECS). To do this, 15 principal effective parameters on rock fragment size were considered in the RRECS modeling process. The uncertainty of the interaction matrix was reduced using Z-number concept. Besides, the weight of effective parameters updated based on the combination of the nonlinear Hebbian algorithm (NLH) and differential evolution algorithm (DE) in FCM. The RRECS performance was validated by statistical linear and non-linear models. The results show R2, RSME, BIAS, and Accuracy using the proposed RRECS model calculated to be 0.957, 1.956, 0.001, and 96.741 for training and 0.931, 0.996, 0.016, and 99.053 for testing parts, respectively. Therefore, RRECS has performed better than the exponential, power, logarithmic, polynomial, and linear models. Furthermore, the sensitivity analysis results revealed that the hole diameter and powder factor parameters have the highest and lowest sensitivity on fragmentation, respectively.
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