Prediction of global stability in room and pillar coal mines

Global stability is a necessary prerequisite for safe retreat mining and one of the crucial and complex problems in room and pillar mining, so its prediction plays an important role in the safety of retreat mining and the reduction of pillar failure risk. In this study, we have tried to develop predictive models for anticipating global stability. For this purpose, two of the most popular techniques, logistic regression analysis and fuzzy logic, were taken into account and a predictive model was constructed based on each. For training and testing of these models, a database including 80 retreat mining case histories from 18 room and pillar coal mines, located in West Virginia State, USA, was used. The models predict global stability based on the major contributing parameters of pillar stability. It was found that both models can be used to predict the global stability, but the comparison of two models, in terms of statistical performance indices, shows that the fuzzy logic model provides better results than the logistic regression. These models can be applied to identify the susceptibility of pillar failure in panels of coal mines, and this may help to reduce the casualties resulting from pillar instability. Finally, the sensitivity analysis was performed on database to determine the most important parameters on global stability. The results revealed that the pillar width is the most important parameter, whereas the depth of cover is the least important one.

[1]  Jhareswar Maiti,et al.  Evaluation of Risk of Occupational Injuries Among Underground Coal Mine Workers Through Multinomial Logit Analysis , 1999 .

[2]  A. A. Campoli,et al.  Analysis of retreat mining pillar stability , 1995 .

[3]  M. Iphar,et al.  An application of fuzzy sets to the Diggability Index Rating Method for surface mine equipment selection , 2006 .

[4]  Masoud Monjezi,et al.  Development of a fuzzy model to predict flyrock in surface mining , 2011 .

[5]  Mohammad Ataei,et al.  Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation , 2012, Arabian Journal of Geosciences.

[6]  H. A. Nefeslioglu,et al.  An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps , 2008 .

[7]  C. Mark,et al.  Coal Mine Roof Rating (CMRR): A practical rock mass classification for coal mines. Information circular/1994 , 1994 .

[8]  Adnan Aydin,et al.  Fuzzy set approaches to classification of rock masses , 2004 .

[9]  Hyuck-Jin Park,et al.  Application of fuzzy set theory to evaluate the probability of failure in rock slopes , 2012 .

[10]  E. H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Man Mach. Stud..

[11]  H. A. Nefeslioglu,et al.  Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey , 2006 .

[12]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[13]  Howard L. Hartman,et al.  Introductory Mining Engineering , 1987 .

[14]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[15]  Kourosh Shahriar,et al.  A new coal pillars design method in order to enhance safety of the retreat mining in room and pillar mines , 2012 .

[16]  Yong-Hun Jong,et al.  Influence of geological conditions on the powder factor for tunnel blasting , 2004 .

[17]  F. Saboya,et al.  Assessment of failure susceptibility of soil slopes using fuzzy logic , 2006 .

[18]  Shobha Kanta Lamichhane,et al.  Application of Logistic Regression Model in Physics Education , 2011 .

[19]  Jian Zhou,et al.  Predicting pillar stability for underground mine using Fisher discriminant analysis and SVM methods , 2011 .

[20]  Christopher Mark,et al.  The Evolution Of Intelligent Coal Pillar Design: 1981-2006; 25th International Conference on Ground Control in Mining , 2007 .

[21]  W. A. Hustrulid,et al.  Underground mining methods handbook , 1982 .

[22]  S. K. Palei,et al.  Logistic regression model for prediction of roof fall risks in bord and pillar workings in coal mines: an approach. , 2009 .

[23]  G. R. Dodagoudar,et al.  RELIABILITY ANALYSIS OF SLOPES USING FUZZY SETS THEORY , 2000 .

[24]  T. Ross.  Fuzzy Logic with Engineering Applications , 1994 .

[25]  Saro Lee.  Application of Likelihood Ratio and Logistic Regression Models to Landslide Susceptibility Mapping Using GIS , 2004, Environmental management.

[26]  Suseno Kramadibrata,et al.  Developing coal pillar stability chart using logistic regression , 2013 .

[27]  O. Acaroglu,et al.  Prediction of thrust and torque requirements of TBMs with fuzzy logic models , 2011 .

[28]  Rafael Jimenez,et al.  A probabilistic extension to the empirical ALPS and ARMPS systems for coal pillar design , 2012 .

[29]  Sophia Decker,et al.  Logistic Regression A Self Learning Text , 2016 .

[30]  Mostafa Sharifzadeh,et al.  Quantifying the uncertainty of pillar safety factor by Monte Carlo simulation - A case study , 2010 .

[31]  M. Kianpour,et al.  A FUZZY MODEL TO PREDICT THE UNIAXIAL COMPRESSIVE STRENGTH AND THE MODULUS OF ELASTICITY OF SHEMSHAK FORMATION SHALES , 2012 .

[32]  S. S. Peng,et al.  Proceedings, 25th international conference on ground control in mining , 2005 .

[33]  M. Ataei,et al.  A new approach to mining method selection based on modifying the Nicholas technique , 2010, Appl. Soft Comput..

[34]  Candan Gokceoglu,et al.  A fuzzy triangular chart to predict the uniaxial compressive strength of the Ankara agglomerates from their petrographic composition , 2002 .

[35]  Robert Babuška,et al.  Fuzzy model for the prediction of unconfined compressive strength of rock samples , 1999 .

[36]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[37]  Christopher Mark,et al.  Reducing the Risk of Ground Falls During Pillar Recovery, C. Mark, F. Chase, and D. Pappas , 1900 .

[38]  Nancy L. Leech,et al.  IBM SPSS for Intermediate Statistics: Use and Interpretation, Fifth Edition , 2014 .

[39]  Syd S. Peng,et al.  Coal Mine Ground Control , 1978 .

[40]  David G. Kleinbaum,et al.  Logistic Regression. A Self- Learning Text , 1994 .

[41]  Candan Gokceoglu,et al.  A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock , 2004, Eng. Appl. Artif. Intell..

[42]  M. S. Rosenbaum.  Neuro-fuzzy modeling in engineering geology , 2001, Quarterly Journal of Engineering Geology and Hydrogeology.