Arabian Journal of Geosciences

, Volume 4, Issue 5–6, pp 845–853 | Cite as

Superiority of neural networks for pillar stress prediction in bord and pillar method

  • M. Monjezi
  • Seyed Masoud Hesami
  • Manoj Khandelwal
Original Paper
First Online:
Received:
Accepted:

Abstract

Estimation of pillar stress is a crucial task in underground mining. This is used to determine pillar dimensions, room width, roof conditions, and general mine layout. There are several methods for estimating induced stresses due to underground excavations, i.e., empirical methods, numerical solutions, and currently artificial intelligence (AI). AI based techniques are gradually gaining popularity especially for problems involving uncertainty. In this paper, an attempt has been made to predict stresses developed in the pillars of bord and pillar mining using artificial neural network. A comparison has also been done to compare the obtained results with the boundary element method as well as measured field values. For this purpose, a multilayer perceptron neural network model was developed. A number of architectures with different hidden layers and neurons were tried to get the best solution, and the architecture 5-20-8-1 was found to be an optimum solution. Sensitivity analysis was also carried out to understand the influence of important input parameters on pillar stress concentration.

Keywords

Artificial intelligence MLP Bord and pillar method 

تفوق الشبكات العصبية للعمود التنبؤ الإجهاد في بورد وأسلوب

بيلار

تقدير الإجهاد دعامة مهمة حاسمة في التعدين تحت الأرض. هذا وتستخدم لتحديد أبعاد العمود ، عرض الغرفة ، وسقف الشروط ، وتخطيط عامة الألغام. هناك عدة طرق لتقدير المستحث ، وتؤكد بسبب الحفريات تحت الأرض ، i. (ه) ، وأساليب تجريبية ، والحلول العددية ، وحاليا الذكاء الاصطناعي (منظمة العفو الدولية). منظمة العفو الدولية تقنيات تدريجيا تكتسب شعبية وخاصة بالنسبة للمشاكل التي تنطوي على عدم اليقين. في هذه الورقة ، وبذلت محاولة للتنبؤ يؤكد المتقدمة في أركان بورد ودعامة التعدين باستخدام الشبكة العصبية الاصطناعية (آن). والمقارنة كما تمت مقارنة النتائج التي تم الحصول عليها على الحدود مع طريقة العناصر المعنية المتوسطة) ، وكذلك قياس قيم الحقل. لهذا الغرض ، تم وضع طبقة متعددة برسبترون (MLP) نموذج الشبكة العصبية. عدد من أبنية مع مختلف الطبقات المخفية والخلايا العصبية وحاول الحصول على أفضل الحلول والعمارة 5-20-8-1 وجد أن الحل الأمثل. تحليل الحساسية كما تم القيام بها لتفهم تأثير المعلمات مدخلا هاما على عمود تركيز الاجهاد. الكلمات الرئيسية : الذكاء الاصطناعي ، وMLP ، بورد والأسلوب عمود ،

This is a preview of subscription content, log in to check access.

References

  1. Becker AA (1992) The boundary element method in engineering. McGraw-Hill, LondonGoogle Scholar
  2. Brebbia CA, Walker S (1978) Introduction to the boundary element method, recent advances in boundary element methods. Brebbia CA (ed), Pentech Press, London, 1-44Google Scholar
  3. Cai JG, Zhao J (1997) Use of neural networks in rock tunneling, Proc. Computer Methods and Advances in Geomechanics. IACMAG, China, 613-618Google Scholar
  4. Cayol Y, Cornet FH (1997) 3D Mixed boundary elements for elastostatic deformation field analysis. Int J Rock Mech Min Sci 34(2):275–287CrossRefGoogle Scholar
  5. Crouch SL, Starfield AM (1983) Boundary element methods in solid mechanics, 2nd edn. George Allen & Unwin, LondonGoogle Scholar
  6. Demuth H, Beal M, Hagan M (1996) Neural network toolbox 5 user’s guide. The Math Work, Inc, NatickGoogle Scholar
  7. Hatzor YH, Benary R (1998) The stability of a laminated Voussoir beam: back analysis of a historic roof collapse using DDA. Int J Rock Mech 35(2):165–181CrossRefGoogle Scholar
  8. Hesami SM (2006) Optimization of tunnel drilling-blasting pattern in limestone with neural networks. M.Sc. dissertation, Tarbiat Modares University, Tehran, Iran (unpublished)Google Scholar
  9. Jaiswal A, Sharma SK, Shrivastva BK (2004) Numerical modeling study of asymmetry in the induced stresses over coal mine pillars with advancement of goafline. Int J Rock Mech Min Sci 41:859–864CrossRefGoogle Scholar
  10. Khandelwal M, Singh TN (2002) Prediction of waste dump stability by an intelligent approach. In: National Symposium on New Equipment—New Technology, Management and Safety, ENTMS, Bhubaneshwar, 38-45Google Scholar
  11. Khandelwal M, Singh TN (2005) Prediction of blast induced air overpressure in opencast mine. Noise Vib Worldwide 36:7–16CrossRefGoogle Scholar
  12. Khandelwal M, Singh TN (2007) Evaluation of blast-induced ground vibration predictors. Soil Dyn Earthqu Eng 27:116–125CrossRefGoogle Scholar
  13. Kim CY, Bae GJ, Hong SW, Park CH, Moon HK, Shin HS (2001) Neural network based prediction of ground surface settlements due to tunneling. Comput Geotech 28:517–547CrossRefGoogle Scholar
  14. Kosko B (1994) Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. Prentice-Hall of India, New Delhi, pp 12–17Google Scholar
  15. Kuriyama K, Mizuta Y (1993) Three dimensional elastic analysis by the displacement discontinuity method with boundary division into triangle leaf elements. Int J Rock Mech Min Sci Geomech Abstr 30(2):111–123CrossRefGoogle Scholar
  16. Kuriyama K, Mizuta Y, Mozumi H, Watanabe T (1995) Three dimensional elastic analysis by the boundary element method with analytical integrations over triangle leaf elements. Int J Rock Mech Min Sci Geomech Abstr 32(1):77–83CrossRefGoogle Scholar
  17. Lee C, Sterling R (1992) Identifying probable failure modes for underground openings using a neural network. Int J Rock Mech Min Sci Geomech Abst 29(1):49–67CrossRefGoogle Scholar
  18. Leu SS, Lin SF, Chen CK, Wang SW (1998) Analysis of powder factors for tunnel blasting using neural networks. Int J Blasting and Fragmentation 2:433–448Google Scholar
  19. Leu SS, Chen CK, Chang SL (2001) Data mining for tunnel support stability: neural network approach. Autom Constr 10(4):429–441CrossRefGoogle Scholar
  20. Li G, Mizuta Y, Ishida T, Li H, Nakama S, Sato T (2009) Stress field determination from local stress measurements. Int J Rock Mech Min Sci 46(1):138–147CrossRefGoogle Scholar
  21. Martin CD, Maybee WG (2000) The strength of hard-rock pillars. Int J Rock Mech Min Sci 37:1239–1246CrossRefGoogle Scholar
  22. Maulenkamp F, Grima MA (1999) Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness. Int J Rock Mech Min Sci 36:29–39CrossRefGoogle Scholar
  23. Monjezi M, Dehghani H (2008) Evaluation of effect of blasting pattern parameters on back break using neural networks. Int J Rock Mech Min Sci 45:1446–1453CrossRefGoogle Scholar
  24. Monjezi M, Singh TN, Khandelwal M, Sinha S, Singh V, Hosseini I (2006a) Prediction and analysis of blast parameters using artificial neural network. Noise Vib Worldw 37:8–16CrossRefGoogle Scholar
  25. Monjezi M, Yazdian A, Hesami SM (2006b) Use of back propagation neural network to estimate burden in tunnel blasting. Mines Met Fuels India 54:424–429Google Scholar
  26. Neaupane KM, Achet SH (2004) Use of back propagation neural network for landslide monitoring: a case study in the higher Himalaya. Eng Geol 74:213–226CrossRefGoogle Scholar
  27. Neaupane KM, Adhikari NR (2006) Prediction of tunneling-induced ground movement with the multi-layer perceptron. Int J Tunneling and Underground Space Technol 21:151–159CrossRefGoogle Scholar
  28. Simpson PK (1990) Artificial neural system-foundation, paradigm, application and implementations. Pergamon Press, New YorkGoogle Scholar
  29. Singh JG (2000) Underground coal mining methods. Brag-Kalpa Publishers, VaranasiGoogle Scholar
  30. Singh VK, Singh D, Singh TN (2001) Prediction of strength properties of some schistose rock. Int J Rock Mech Min Sci 38:269–284CrossRefGoogle Scholar
  31. Tawadrous AS, Katsabanis PD (2005) Prediction of surface blast patterns in limestone quarries using artificial neural networks. Fragblast Int J Blast Fragment 10:233–242CrossRefGoogle Scholar
  32. Tawadrous AS, Katsabanis PD (2006) Prediction of surface crown pillar stability using artificial neural networks. Int J Numer Anal Methods Geomech 31:917–931CrossRefGoogle Scholar
  33. Tawadrous A, Katsabanis PD (2007) Prediction of surface crown pillar stability using artificial neural networks. Int J Numer Anal Methods Geomech 31(7):917–931CrossRefGoogle Scholar
  34. Yang Y, Zhang Q (1997) A hierarchical analysis for rock engineering using artificial neural networks. Rock Mech Rock Engng 30(4):207–222CrossRefGoogle Scholar
  35. Watson JO, Cowling R (1985) Applications of three-dimensional boundary element method to modelling of large mining excavations at depth. Proceedings 5th International Symposium Numerical Methods in Geomechanics. Balkema, RotterdamGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2009

Authors and Affiliations

  • M. Monjezi
    • 1
  • Seyed Masoud Hesami
    • 1
  • Manoj Khandelwal
    • 2
  1. 1.Faculty of EngineeringTarbiat Modares UniversityTehranIran
  2. 2.Department of Mining EngineeringMaharana Pratap University of Agriculture and TechnologyUdaipurIndia

Personalised recommendations