Artificial Intelligence Model for Prediction of Local and Main FALL in caving Panel of Bord and Pillar Method of Mining

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Authors

  • Department of Mining Engineering, Indian Institute of Technology (ISM), Dhanbad – 826004, Jharkhand ,IN
  • Department of Mining Engineering, Indian Institute of Technology (ISM), Dhanbad – 826004, Jharkhand ,IN
  • National Institute of Rock Mechanics, Bangalore – 560070, Karnataka ,IN
  • Department of Mining Engineering, Indian Institute of Technology (ISM), Dhanbad – 826004, Jharkhand ,IN
  • All India Institute of Medical Sciences, Patna – 801507, Bihar ,IN

DOI:

https://doi.org/10.18311/jmmf/2022/30018

Keywords:

Bord and Pillar, Caving, Deep Learning Algorithm, Deep Neural Network, Hyper Parameter Optimization, Local Fall, Main Fall, Talos

Abstract

Depillaring with caving method of mining is a common practice in Indian coalfields and so is the occurrence of fall in goaf area, which can be considered as a boon in disguise as it allows wining of coal from large reserves but this becomes a curse just because of its unpredicted occurrence. Various empirical and statistical models are developed after idealization of several complicated mechanisms but they are not able to predict roof fall accurately especially in caving panels. Therefore, a new approach based on Artificial Intelligence is used to predict the sequence of local and main fall in caving panel taking into account a host of geotechnical and mining parameters of the mine. Mathematical equations and hidden calculations of artificial neural networks are known to have the capability of learning and analyzing records endlessly. Two different models have been deployed after optimal hyper parameter optimization to predict the occurrence of fall and to characterize the nature of fall (local or main) with considerable and reliable accuracy.

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Published

2022-06-20

How to Cite

Bilash Prajapati, R., Kumar Sinha, R., Gupta, R. N., Kumar, S., & Prajapati, D. (2022). Artificial Intelligence Model for Prediction of Local and Main FALL in caving Panel of Bord and Pillar Method of Mining. Journal of Mines, Metals and Fuels, 70(4), 171–181. https://doi.org/10.18311/jmmf/2022/30018

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References

Simon, H.A. (1957). Models of Man: Social and Rational. John Wiley and Sons, Inc., 1957.

Malkowski, P., & Juszynski, D. (2021). Roof fall hazard assessment with the use of artificial neural network. International Journal of Rock Mechanics & Mining Sciences 143(2021): 104701. https://doi.org/10.1016/j.ijrmms.2021.104701

Isleyen, E., Duzgun, S., & Carter, R. (2021). Interpretable deep learning for roof fall hazard detection in underground mines. Journal of Rock Mechanics and Geotechnical Engineering, 13(6): 1246–1255. https://doi.org/10.1016/j.jrmge.2021.09.005

Razani, M.,Yazdani-chamzini, A., & Yakhchali, S. (2013). A novel fuzzy inference system for predicting roof fall rate in underground coal mines. Safety Science, 55: 26–33. https://doi.org/10.1016/j.ssci.2012.11.008

Deb, D., Kumar, A., & Rosha, R. (2006). Forecasting shield pressures at a longwall face using artificial neural networks. Geotechnical and Geological Engineering, 24: 1021–1037. https://doi.org/10.1007/s10706-005-4430-6

Monjezi, M., Hesami, S., & Khandelwal, M. (2009). Superiority of neural networks for pillar stress prediction in bord and pillar method. Arabian Journal of Geosciences, 4: 845–853. https://doi.org/10.1007/s12517-009-0101-x

Sheorey, P.R. (1984). Use of rock classification to estimate roof caving span in oblong workings. International Journal of Mining and Mineral Engineering, 2: 133–140. https://doi.org/10.1007/BF00880878

Singh, R., Singh, T.N., & Dhar, B.B. (1996). Coal pillar loading in shallow mining conditions. International Journal of Rock Mechanics and Mining Sciences & Geomechanics, 33, No.8: 757-768.

Sheorey P.R. (1994). A Theory for in situ stresses in isotropic and transversely isotropic. International Journal of Rock Mechanics and Mining Sciences & Geomechanics, 31(1): 23–34. https://doi.org/10.1016/0148-9062(94)92312-4

Jena, S., Prasad, K., Lokhande, R.D., & Pradhan, M. (2016). Analysis of strata control monitoring in underground coal mine for apprehension of strata movement. Recent Advances in Rock Engineering (RARE 2016), pp. 505–511. https://doi.org/10.2991/rare-16.2016.81

Obert, L., & Duvall, W. (1967). Rock mechanics and the design of structures in rock. New York: John Wiley and Sons, Inc.; 1967.

Majumdar, S. (1986). The support requirement at a longwall face — bending moment approach. In: Proceedings of 27th US Symposium on Rock Mechanics: Key to Energy Production (The University of Alabama, Tuscaloosa, Alabama); 1986: 325–332.

Pawlowicz, K. (1967). Classification of rock cavability of coal measure strata in upper Silesia coalfield. Prace GIG, Komunikat, No. 429, Katowice. (in Polish).

Peng, S.S., & Chiang, H.S. (1984). Longwall mining. In New York: John Wiley and Sons, Inc.

Ghose, A.K., & Dutta, D. (1987). A rock mass classification model for caving roofs. International Journal of Mining and Geological Engineering, 5: 257–271. https://doi.org/10.1007/BF01560777

Sarkar, S.K. (1998). Mechanized longwall mining — The Indian experiences. New Delhi: Oxford and IBH Publishing Company Private Limited.

Sarkar, S.K., & Dhar, B.B. (1993). Strata control failures at caved longwall faces in India — experience from Rana to Churcha (1964 to 1990). In Proceedings of the 4th Asian Mining (Organized by MGMI at Calcutta), 361–380.

Nimaje, D.S., & Sai, S. (2015). Development of software to evaluate roof fall risk in bord and pillar method — Depillaring Phase. GeoScience Engineering, LXI(2): 14–22. https://doi.org/10.1515/gse-2015-0014

McCulloch, W., & Pitts, W. (1943). A logical calculus of ideas immanent in nervous activity. The Bulletin of Math. Biophys., 5: 115–133. https://doi.org/10.1007/BF02478259

Fausett, L.V. (1993). Fundamentals of Neural Networks:Architectures, Algorithms and Applications. (1st ed). Pearson publication, India.

Hopfield, J.J. (1982). Neural networks and physical systems with emergent collective computational capabilities. In Proceedings of National Academy of Sciences, (USA), 79: 2554–2558. https://doi.org/10.1073/pnas.79.8.2554. PMid:6953413. PMCid:PMC346238

Hopfield, J.J. (1984). Neurons with graded responses have collective computational properties like those of twostate neurons. In Proceedings of National Academy of Sciences (USA), 81: 3088–3092. https://doi.org/10.1073/pnas.81.10.3088. PMid:6587342. PMCid:PMC345226

Lee, S., Ryu, J., Lee, M., & Won, J. (2003). Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea. Environmental Geology, 44(7): 820–833. https://doi.org/10.1007/s00254-003-0825-y

Russell, S.J., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (Third ed). Pearson India Education Services Pvt. Ltd.

Haykin, S. (1999). Neural Networks:A Comprehensive Foundation. (2nd Ed.).

Nazzal, J., El-Emary, I., & Najim, S. (2008). Multilayer Perceptron Neural Network (MLPs) for analyzing the properties of Jordan Oil Shale. World Applied Sciences Journal, 5(5): 546–552.

Chollet, F., & Others. (2015). Keras. GitHub.

Van Rossum, G., & Drake, F.L. (2009). Python 3 Reference Manual.

Bisong, E. (2019). Google colaboratory. In: Building machine learning and deep learning models on Google cloud platform. (Apress Berkeley CA., pp. 59–64). https://doi.org/10.1007/978-1-4842-4470-8_7

Talos, A. (2019). Autonomio Talos [Computer Software] hyperparameter optimization for tensorflow, Keras and Pytorch.

Parashar, A., & Sonker, A. (2019). Application of hyperparameter optimized deep learning neural network for classification of air quality data. International Journal of Scientific & Technology Research, 8(11): 1435–1443.

Ghasemi, E., Ataei, M., Shahriar, K., Sereshki, F., & Esmaeil, S. (2012). Assessment of roof fall risk during retreat mining in room and pillar coal mines. International Journal of Rock Mechanics and Mining Science, 54: 80–89. https:// doi.org/10.1016/j.ijrmms.2012.05.025

Kumar, A., Kumar, D., Singh, A.K., Ram, S., Kumar, R., Gautam, A., Singh, R., & Singh, A.K. (2019). Roof sagging limit in an early warning system for safe coal pillar extraction. International Journal of Rock Mechanics and Mining Sciences, 123: 104–131. https://doi.org/10.1016/j. ijrmms.2019.104131

Mark, C., & Michael, G. (2017). Preventing roof fall fatalities during pillar recovery: A ground control success story. International Journal of Mining Science and Technology, 27(1): 107–113. https://doi.org/10.1016/j.ijmst.2016.09.030

Mark, C., & Molinda, G. (2007). Development and application of the Coal Mine Roof Rating (CMRR). Proceedings of the International Workshop on Rock Mass Classification in Underground Mining, 95–110.

Palei, S.K., & Das, S.K. (2009). Logistic regression model for prediction of roof fall risks in bord and pillar workings in coal mines:An approach. Safety Science, 47: 88–96. https://doi.org/10.1016/j.ssci.2008.01.002

Torres, J. (2018). First contact with Deep Learning, Practical introduction with Keras.

Fan, C., Chen, M., Wang, X., Wang, J. & Huang, B. (2021). A review on data preprocessing techniques toward efficient and reliable knowledge discovery from building operational data. Sustainable Energy Systems and Policies, Frontiers in Energy Research, 9: 18. https://doi.org/10.3389/ fenrg.2021.652801

Brownlee, J. (2020). Data preparation for machine learning:Data cleaning, feature selection and data transforms in Python.

Godoy, D. (2018). Understanding binary cross-entropy/ log loss: A visual explanation.

Kalantar, B., Pradhan, B., Naghibi, S.A., Motevalli, A., & Mansor, S. (2018). Assessment of the effects of training data selection on the landslide susceptibility mapping: A comparison between Support Vector Machine (SVM), Logistic Regression (LR) and Artificial Neural Networks (ANN). Geomatics, Natural Hazards and Risk, 9(1): 49–69. https://doi.org/10.1080/19475705.2017.1407368

Goutte, C., & Gaussier, E.(2005). A probabilistic interpretation of precision, recall and F -score with implication for evaluation. Lecture Notes in Computer Science, 3408: 345–359. https://doi.org/10.1007/978-3-540-31865-1_25

Bradley, A.E. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7): 1145–1159. https://doi.org/10.1016/S0031-3203(96)00142-2