A fuzzy rock engineering system to assess rock mass cavability in block caving mines. Issue 7 (October 2016)

• Record Type: Journal Article
• Authors: Rafiee, R.; Ataei, Mohammad; KhaloKakaie, Reza; Jalali, S.; Sereshki, F.
• Is Part Of: Neural computing & applications. Volume 27:Issue 7(2016)
• Page Start: 2083
• Page End: 2094
• Publication Date: 2016-10
• Subjects: Cavability -- FESQ coding -- Fuzzy system -- Rock engineering system; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-2007-8
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract An improvement for rock engineering system (RES) coding method is done in this paper by using fuzzy systems. A fuzzy expert semi-quantitative coding methodology is designed to assess the cavability of rock mass within the RES framework. The proposed fuzzy method has the advantage of allowing consideration of uncertainties in the RES analysis by using membership functions in comparison with classic expert semi-quantitative coding method that only unique codes are used to quantify the interaction matrix. Since the cavability of the rock mass is one of the fundamental issues for the caving mining method, the presented improved coding method is creatively used to assess the influencing parameters on cavability of rock mass in block caving mines. Fifteen parameters are considered as the main factors modeling the cavability of the rock mass, and the interactions between these parameters are calculated by proposed fuzzy system. Finally in this paper, the parameters, which are dominant or subordinant, and also the parameters, which are interactive, are introduced. The proposed approach could be a simple but efficient tool in evaluation of the parameters affecting the cavability of rock mass in block caving mines and hence be useful in decision making under uncertainties.