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Development of an intelligent model to estimate the height of caving–fracturing zone over the longwall gobs

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Abstract

After the ore (seam) extraction in longwall mining, the immediate roof layers over the extracted panel are strained and suspended downward. This process expands upward and causes the caving and fracturing of damaged roof rock strata. The combination height of the caved and interconnected fractured zones is considered as the height of caving–fracturing zone (HCFZ) in this research. Precise estimation of this height is crucial to the exact determination of directed loads toward the front and sides abutments. The paper describes an intelligent model based on the artificial neural network (ANN) to predict HCFZ. To validate the ability of ANN model, its results are compared to the multivariable regression analysis (MVRA) results. For models construction and evaluation, a wide range of datasets comprising of geometrical and geomechanical characteristics of mined panel and roof strata have been gathered. Performance evaluation indices including determination coefficient (R 2), variance account for, mean absolute error (E a) and mean relative error (E r) have been utilized to assess the models’ capability. Comparison results show that the ANN model performance is considerably better than the MVRA model. Moreover, obtained results are further compared with the results of available in situ, empirical, analytical, numerical and physical models reported in the literature. This comparison confirms that a reasonable agreement exists between the ANN model and the previous comparable methods. Finally, the sensitivity analysis of ANN results shows that the overburden depth has the maximum effect, whereas the Poisson’s ratio has the minimum effect on the HCFZ in this research.

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References

  1. Majdi A, Hassani FP, Yousef Nasiri M (2012) Prediction of the height of destressed zone above the mined panel roof in longwall coal mining. Int J Coal Geol 98(1):62–72

    Article  Google Scholar 

  2. Rezaei M, Farouq Hossaini M, Majdi A (2015) A time-independent energy model to determine the height of destressed zone above the mined panel in longwall coal mining. Tunn Undergr Space Technol 47:81–92

    Article  Google Scholar 

  3. Rezaei M, Farouq Hossaini M, Majdi A (2015) Determination of longwall mining-induced stress using the strain energy method. Rock Mech Rock Eng 48(6):2421–2433

    Article  Google Scholar 

  4. Rezaei M, Farouq Hossaini M, Majdi A (2015) Development of a time-dependent energy model to calculate the mining-induced stress over gates and pillars. J Rock Mech Geotech Eng 7(3):306–317

    Article  Google Scholar 

  5. Neaupane KM, Achet SH (2004) Use of backpropagation neural network for landslide monitoring: a case study in the higher Himalaya. Eng Geol 74(3–4):213–226

    Article  Google Scholar 

  6. Khandelwal M, Singh TN (2006) Prediction of blast induced ground vibrations and frequency in opencast mine—a neural network approach. J Sound Vib 289(4–5):711–725

    Article  Google Scholar 

  7. Khoshjavan S, Mazlumi M, Rezai B, Rezai M (2010) Estimation of hardgrove grindability index (HGI) based on the coal chemical properties using artificial neural networks. Orient J Chem 26(4):1271–1280

    Google Scholar 

  8. Bahrami A, Monjezi M, Goshtasbi K, Ghazvinian A (2011) Prediction of rock fragmentation due to blasting using artificial neural network. Eng Comput 27(2):177–181

    Article  Google Scholar 

  9. Rezaei M, Monjezi M, Ghorbani Moghaddam S, Farzaneh F (2012) Burden prediction in blasting operation using rock geomechanical properties. Arab J Geosci 5(5):1031–1037

    Article  Google Scholar 

  10. Majdi A, Rezaei M (2013) Prediction of unconfined compressive strength of rock surrounding a roadway using artificial neural network. Neural Comput Appl 23(2):381–389

    Article  Google Scholar 

  11. Majdi A, Rezaei M (2013) Application of artificial neural networks for predicting the height of destressed zone above the mined panel in longwall coal mining. In: 47th U.S. rock mechanics/geomechanics symposium, 23–26 June, San Francisco, California, USA, pp 1665–1673

  12. Sayadi AR, Tavassoli SMM, Monjezi M, Rezaei M (2014) Application of neural networks to predict net present value in mining projects. Arab J Geosci 7(3):1067–1072

    Article  Google Scholar 

  13. Saghatforoush A, Monjezi M, Shirani Faradonbeh R, Armaghani Jahed D (2016) Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting. Eng Comput 32(2):255–266

    Article  Google Scholar 

  14. Gordan B, Armaghani Jahed D, Hajihassani M, Monjezi M (2016) Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng Comput 32(1):85–97

    Article  Google Scholar 

  15. Verma AK, Singh TN, Monjezi M (2010) Intelligent prediction of heating value of coal. Iran J Earth Sci 2:32–38

    Google Scholar 

  16. Singh TN, Verma AK (2012) Comparative analysis of intelligent algorithms to correlate strength and petrographic properties of some schistose rocks. Eng Comput 28(1):1–12

    Article  Google Scholar 

  17. Verma AK (2014) A comparative study of various empirical methods to estimate the factor of safety of coal pillars. Am J Min Metall 2(1):17–22

    Google Scholar 

  18. Verma AK, Maheshwar S (2014) Comparative study of intelligent prediction models for pressure wave velocity. J Geosci Geomat 2(3):130–138

    Google Scholar 

  19. Bhatnagar A, Khandelwal M (2012) An intelligent approach to evaluate drilling performance. Neural Comput Appl 21(4):763–770

    Article  Google Scholar 

  20. Salsani A, Daneshian J, Shariati S, Yazdani-Chamzini A, Taheri M (2014) Predicting roadheader performance by using artificial neural network. Neural Comput Appl 24(7):1823–1831

    Article  Google Scholar 

  21. Stojadinović S, Lilić N, Obradović I, Pantović R, Denić M (2016) Prediction of flyrock launch velocity using artificial neural networks. Neural Comput Appl 27(2):515–524

    Article  Google Scholar 

  22. Yari M, Bagherpour R, Jamali S, Shamsi R (2016) Development of a novel flyrock distance prediction model using BPNN for providing blasting operation safety. Neural Comput Appl 27(3):699–706

    Article  Google Scholar 

  23. Mahdevari S, Shahriar K, Sharifzadeh M, Tannant DD (2016) Stability prediction of gate roadways in longwall mining using artificial neural networks. Neural Comput Appl. doi:10.1007/s00521-016-2263-2

    Article  Google Scholar 

  24. Singh MM, Kendorski FS (1981) Strata disturbance prediction for mining beneath surface water and waste impoundments. In: Proceedings of the first conference on ground control in mining, Uni. West Virginia, pp 76–89

  25. Karmis M, Triplett T, Haycocks C, Goodman G, (1983) Mining subsidence and its prediction in an Appalachian coalfield. Rock mechanics: theory, experiment, practice. In: Proceedings of the 24th US symposium rock mechanics, 20–23 June 1983, Texas A&M University. Balkema, Rotterdam, pp 665–675

  26. Hasenfus GJ, Johnson KL, Su DWH (1998) A hydrogeomechanical study of overburden aquifer response to longwall mining. In: Peng Syd S (ed) Proceedings of the 7th international conference on ground control in mining. West Virginia University, COMER, Department of Mining Engineering, Morgantown, pp 149–162

  27. Booth CJ, Spande ED (1992) Potentiometric and aquifer property changes above subsiding longwall mine panels, Illinois basin coalfield. Groundwater 30(3):362–368

    Article  Google Scholar 

  28. Peng SS (1992) Surface subsidence engineering. The society for mining, metallurgy and exploration, ground control (Mining), p 161

  29. Chekan G, Listak J (1993) Design practices for multiple-seam longwall mines. Information circular 9360. U.S. Dept. of the Interior, Bureau of Mines, Pittsburgh, PA, p 35

    Google Scholar 

  30. Palchik V (2002) Influence of physical characteristics of weak rock mass on height of caved zone over abandoned subsurface coal mines. Environ Geol 42(1):92–101

    Article  Google Scholar 

  31. Palchik V (2003) Formation of fractured zones in overburden due to longwall mining. Environ Geol 44(1):28–38

    Article  Google Scholar 

  32. Palchik V (2010) Experimental investigation of apertures of mining-induced horizontal fractures. Int J Rock Mech Min Sci 47(3):502–508

    Article  Google Scholar 

  33. Tajduś K (2015) Analysis of horizontal displacement distribution caused by single advancing longwall panel excavation. J Rock Mech Geotech Eng 7(3):395–403

    Article  Google Scholar 

  34. Xue J, Wang H, Zhou W, Ren B, Duan C, Deng D (2015) Experimental research on overlying strata movement and fracture evolution in pillarless stress-relief mining. Int J Coal Sci Technol 2(1):38–45

    Article  Google Scholar 

  35. Bai JB, Shen WL, Guo GL, Wang XY, Yu Y (2015) Roof deformation, failure characteristics, and preventive techniques of gob-side entry driving heading adjacent to the advancing working face. Rock Mech Rock Eng 48(6):2447–2458

    Article  Google Scholar 

  36. Ming-he J, Xue-hua L, Qiang-ling Y, Dong-wei L, Zhao-hui C, Jian Z (2015) Numerical investigation into effect of rear barrier pillar on stress distribution around a longwall face. J Cent South Univ 22(11):4372–4384

    Article  Google Scholar 

  37. Palchik V (2015) Bulking factors and extents of caved zones in weathered overburden of shallow abandoned underground workings. Int J Rock Mech Min Sci 79:227–240

    Article  Google Scholar 

  38. Qu Q, Xu J, Wu R, Qin W, Hu G (2015) Three-zone characterisation of coupled strata and gas behaviour in multi-seam mining. Int J Rock Mech Min Sci 78:91–98

    Article  Google Scholar 

  39. Yu B, Zhao J, Kuang T, Meng X (2015) In situ investigations into overburden failures of a super-thick coal seam for longwall top coal caving. Int J Rock Mech Min Sci 78:155–162

    Article  Google Scholar 

  40. Jiachen W, Shengli Y, Dezhong K (2016) Failure mechanism and control technology of longwall coalface in large-cutting-height mining method. Int J Min Sci Technol 26(1):111–118

    Article  Google Scholar 

  41. Meng Z, Shi X, Li G (2016) Deformation, failure and permeability of coal-bearing strata during longwall mining. Eng Geol 208(24):69–80

    Article  Google Scholar 

  42. Zhu S, Yu F, Jiang F (2016) Determination of abutment pressure in coal mines with extremely thick alluvium stratum: a typical kind of rockburst mines in China. Rock Mech Rock Eng 49(5):1943–1952

    Article  Google Scholar 

  43. Yu B, Zhang Z, Kuang T, Liu J (2016) Stress changes and deformation monitoring of longwall coal pillars located in weak ground. Rock Mech Rock Eng 49(8):3293–3305

    Article  Google Scholar 

  44. Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 43(1):3–31

    Article  Google Scholar 

  45. Demuth H, Beal M, Hagan M (1996) Neural network tool box 5 user’s guide. The Math Work, Natick

    Google Scholar 

  46. Jennrich RI (1995) An introduction to computational statistics-regression analysis. Prentice Hall, Englewood Cliffs, NJ

    MATH  Google Scholar 

  47. Tzamos S, Sofianos AI (2006) Extending the Q system’s prediction of support in tunnels employing fuzzy logic and extra parameters. Int J Rock Mech Min Sci 43(6):938–949

    Article  Google Scholar 

  48. Rezaei M, Majdi A, Monjezi M (2014) An intelligent approach to predict unconfined compressive strength of rock surrounding access tunnels in longwall coal mining. Neural Comput Appl 24(1):233–241

    Article  Google Scholar 

  49. Rezaei M, Asadizadeh M, Majdi A, Farouq Hossaini M (2015) Prediction of representative deformation modulus of longwall panel roof rock strata using Mamdani fuzzy system. Int J Min Sci Technol 25(1):23–30

    Article  Google Scholar 

  50. Fawcett RJ, Hibberd S, Singh RN (1986) Analytic calculations of hydraulic conductivities above longwall coal face. Int J Mine Water 5(1):45–60

    Article  Google Scholar 

  51. Follington IL, Isaac AK (1990) Failure zone development above longwall panels. Min Sci Technol 10(2):103–116

    Article  Google Scholar 

  52. Mills K, O’Grady P (1998) Impact of longwall width on overburden behavior. In: Aziz N (ed) Coal 98: Coal operators’ conference, University of Wollongong and The Australasian Institute of Mining and Metallurgy, pp 147–155

  53. Jong YH, Lee CI (2004) Influence of geological conditions on the powder factor for tunnel blasting. Int J Rock Mech Min Sci 41(1):533–538

    Article  Google Scholar 

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  1. Department of Mining Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran

    Mohammad Rezaei

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Correspondence to Mohammad Rezaei.

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Rezaei, M. Development of an intelligent model to estimate the height of caving–fracturing zone over the longwall gobs. Neural Comput & Applic 30, 2145–2158 (2018). https://doi.org/10.1007/s00521-016-2809-3

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  • DOI: https://doi.org/10.1007/s00521-016-2809-3

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