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The purpose of this study is the application of meta-heuristic algorithms and fuzzy logic in the optimization and clustering to predict the sawability of dimension stone. Survey and classification of dimension stones based on their physical and mechanical properties can be so impressive in the optimization of machine applications that are in this industry such as circular diamond saw block cutting machine. In this paper, physical and mechanical properties were obtained from laboratory testing on dimension stone block samples collected from 12 quarries located in Iran and their results were optimized and classified by one of the strongest meta-heuristic algorithms and fuzzy clustering technique. The clustering of dimension stone was determined by Lloyd’s algorithm (k-means clustering) based on imperialist competitive algorithm and fuzzy C-mean by MATLAB software. The hourly production rate of each studied dimension stones was considered as a criterion to evaluate the clustering efficacy. The results of this study showed that the Imperialist Competitive algorithm and fuzzy C-mean are very suitable for clustering with respect to the physical and mechanical properties of the dimension stone, and the results obtained showed the superiority of the ICA. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Roof fall is one of the serious hazards associated with underground coal mining. Roof fall can cause fatal and non-fatal injuries on miners, stoppages in mining operations and equipment breakdowns. Therefore, accurate prediction of roof fall rate is very important in controlling and eliminating of related problems. In this study, the fuzzy logic was applied to predict roof fall rate in coal mines. The predictive fuzzy model was implemented on fuzzy logic toolbox of MATLAB using Mamdani algorithm and was developed based on experts' knowledge and also a database including 109 datasets of roof performance from US coal mines. 22 datasets of this database were used to assess the performance of this fuzzy model. The comparison between obtained results from model and actual roof fall rate showed that the fuzzy model can predict roof fall rate very well. [ABSTRACT FROM AUTHOR]