TY - JOUR ID - 1160 TI - Prediction of the main caving span in longwall mining using fuzzy MCDM technique and statistical method JO - Journal of Mining and Environment JA - JME LA - en SN - 2251-8592 AU - Mohammadi, S. AU - Ataei, M. AU - Khaloo Kakaie, R. AU - Mirzaghorbanali, A. AD - Faculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran AD - School of Civil Engineering & Surveying, University of Southern Queensland, Toowoomba, Queensland, Australia Y1 - 2018 PY - 2018 VL - 9 IS - 3 SP - 717 EP - 726 KW - Main Caving Span KW - Cavability Index KW - Longwall KW - Multi-Criteria Decision-Making KW - Regression Analysis DO - 10.22044/jme.2018.6715.1492 N2 - Immediate roof caving in longwall mining is a complex dynamic process, and it is the core of numerous issues and challenges in this method. Hence, a reliable prediction of the strata behavior and its caving potential is imperative in the planning stage of a longwall project. The span of the main caving is the quantitative criterion that represents cavability. In this paper, two approaches are proposed in order to predict the span of the main caving in longwall projects. Cavability index (CI) is introduced based on the hybrid multi-criteria decision-making technique, combining the fuzzy analytical network processes (ANP) and the fuzzy decision-making trial and evaluation laboratory (DEAMTEL). Subsequently, the relationship between the new index and the caving span is determined. In addition, statistical relationships are developed, incorporating the multivariate regression method. The real data for nine panels is used to develop the new models. Accordingly, two models based on CI including the Gaussian and cubic models as well as the linear and non-linear regression models are proposed. The performance of the proposed models is evaluated in various actual cases. The results obtained indicate that the CI-Gaussian model possesses a higher performance in the prediction of the main caving span in actual cases when compared to the other models. These results confirm that it is not possible to consider all the effective parameters in an empirical relationship due to a higher error in the prediction. UR - https://jme.shahroodut.ac.ir/article_1160.html L1 - https://jme.shahroodut.ac.ir/article_1160_6b917a0e92bdc51bee198f93315f821c.pdf ER -