Top

Neural Computing and Applications

Published in:

01-09-2012 | Original Article

Artificial neural network for prediction of air flow in a single rock joint

Authors: P. G. Ranjith, Manoj Khandelwal

Published in: Neural Computing and Applications | Issue 6/2012

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

In this paper, an attempt has been made to evaluate and predict the air flow rate in triaxial conditions at various confining pressures incorporating cell pressure, air inlet pressure, and air outlet pressure using artificial neural network (ANN) technique. A three-layer feed forward back propagation neural network having 3-7-1 architecture network was trained using 37 data sets measured from laboratory investigation. Ten new data sets were used for the, validation and comparison of the air flow rate by ANN and multi-variate regression analysis (MVRA) to develop more confidence on the proposed method. Results were compared based on coefficient of determination (CoD) and mean absolute error (MAE) between measured and predicted values of air flow rate. It was found that CoD between measured and predicted air flow rate was 0.995 and 0.758 by ANN and MVRA, respectively, whereas MAE was 0.0413 and 0.1876.

previous article Transient chaotic neural network-based disjoint multipath routing for mobile ad-hoc networks

next article Direct adaptive robust NN control for a class of discrete-time nonlinear strict-feedback SISO systems

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Darcy H (1856) Les Fontaines Publiques de la Ville de Dijon. Victor Dalmond, Paris

Forchheimer P (1901) Wasserbewegung durch Boden. Z Ver Deutsch Ing 45:1782–1788

Bear J (1972) Dynamics of fluids in porous media. Elsevier, New YorkMATH

Hannoura A, Barends FBJ (1981) Non-Darcy flow: a state of art. In: Verruijt A, Barends FBJ (eds) Proceedings of the euromech 143. Balkema PC, Rotterdam, pp 37–51

Scheidegger AE (1974) The physics of flow through porous media, 3rd edn. University of Toronto Press, Toronto

Pyrak-Nolte LJ, Myer LR, Cook NGW, Witherspoon PA (1987) Hydraulic and mechanical properties of natural fractures in low permeability rock. In: 6th International congress rock mechanics, ISRM, Montreal, Canada, pp 225–231

Raven KG, Gale JE (1985) Water flow in a natural rock fracture as a function of stress and sample size. Int J Rock Mech Min Sci Geomech Abstr 16:225–234

Durham WB, Bonner BP (1994) Self-propping and fluid flow in slightly offset joints at high effective pressures. J Geophys Res 99:9391–9399CrossRef

Jaeger JC, Cook NGW (1976) Fundamentals of rock mechanics. Chapman and Hall, London, pp 169–171

10.

Brown SR (1987) Fluid flow through rock joints: the effect of surface roughness. J Geophys Res 92:1337–1347CrossRef

11.

Pratt HR, Swolfs HS, Brace WF, Black AD, Handin JW (1977) Elastic and transport properties of an in situ jointed granite. Int J Rock Mech Min Sci Geomech Abstr 14:34–45

12.

Tsang YW (1984) The effect of tortuosity on fluid flow through a single fracture. Water Resour Res 20:1205–1215CrossRef

13.

Zimmerman RW, Chen DW, Cook NGW (1992) The effect of contact area on the permeability of fractures. J Hydrol 139:79–96CrossRef

14.

Durham WB (1997) Laboratory observations of the hydraulic behavior of a permeable fracture from 3800 m depth in the KTB pilot hole. J Geophys Res 102:18405–18416CrossRef

15.

Oron AP, Berkowitz B (1998) Flow in rock fractures: the local cubic law assumption reexamined. Water Resour Res 34:2811–2825CrossRef

16.

Skjetne E, Hansen A, Gudmundsson JS (1999) High-velocity flow in a rough fracture. J Fluid Mech 383:1–28MATHCrossRef

17.

Zimmerman RW, Yeo IW (2000) Fluid flow in rock fractures: from the Navier–Stokes equations to the cubic law. In: Dynamics of fluids in fractured rock, geophysical monograph 122, American geophysical union, pp 213–224

18.

Iwai K (1976) Fundamental studies of fluid flow through a single fracture. PhD dissertation, University of California, Berkeley, California

19.

Al-Yaarubi A (2003) Numerical and experimental study of fluid flow in a rough-walled rock fracture. PhD dissertation, Imperial College, London

20.

Zimmerman RW, Al-Yaarubi A, Pain CC, Grattoni CA (2004) Non-linear regimes of fluid flow in rock fractures. Int J Rock Mech Min Sci 41(supplement A):163–169

21.

Wittke W (1990) Rock mechanics: theory and applications with case histories. Springer, Berlin

22.

Lee CH, Farmer I (1993) Fluid flow in discontinuous rocks. Chapman and Hall, London

23.

Khandelwal M, Roy MP, Singh PK (2004) Application of artificial neural network in mining industry. Ind Min Eng J 43:19–23

24.

Rajasekaran S, Pai GAV (2005) Neural networks, fuzzy logic and genetic algorithms: synthesis and applications. Prentice Hall, New Delhi

25.

Khandelwal M (2002) Application of neural network for the prediction of triaxial constants from uniaxial compressive strength. MTech dissertation, Department of Mining Engineering, Banaras Hindu University, Varanasi, India

26.

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 36:29–39CrossRef

27.

Yang Y, Zhang Q (1997) Analysis for the results of point load testing with artificial neural network. In: Proceedings of the computer methods and advances in geomechanics, IACMAG, China, pp 607–612

28.

Cai JG, Zhao J (1997) Use of neural networks in rock tunneling. Proceedings of the computer methods and advances in geomechanics, IACMAG, China, pp 613–618

29.

Khandelwal M, Singh TN (2002) Prediction of waste dump stability by an intelligent approach. National symposium new equipment—new technology, management and safety, ENTMS, Bhubaneshwar, pp 38–45

30.

Maity D, Saha A (2004) Damage assessment in structure from changes in static parameters using neural networks. Sadhana 29:315–327CrossRef

31.

Khandelwal M, Singh TN (2009) Prediction of blast induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46(7):1214–1222CrossRef

32.

Khandelwal M, Singh TN (2010) Prediction of macerals contents of Indian coals from proximate and ultimate analyses using artificial neural network. Fuel 89(5):1101–1109CrossRef

33.

Khandelwal M, Singh TN (2010) Artificial neural network as a valuable tool for well log interpretation. Petroleum Sci Technol 28(14):1381–1393CrossRef

34.

Bhatnagar A, Khandelwal M (2010) An intelligent approach to evaluate drilling performance. Neural Comput Appl. doi:10.1007/s00521-010-0457-6 (online published)

35.

Monjezi M, Bahrami A, Yazdian Varjani A (2010) Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks. Int J Rock Mech Min Sci 47(3):476–480CrossRef

36.

Monjezi M, Ahmadi M, Sheikhan M, Bahrami A, Salimi AR (2010) Predicting blast-induced ground vibration using various types of neural networks. Soil Dyn Earthq Eng 30(11):1233–1236CrossRef

37.

Simpson PK (1990) Artificial neural system—foundation, paradigm, application and implementations. Pergamon Press, New York

38.

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

39.

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(8):1446–1453CrossRef

40.

Lanaro F (2000) A random field model for surface roughness and aperture of rock fractures. Int J Rock Mech Min Sci 37:1195–1210CrossRef

41.

Zimmerman RW, Bodvarsson GS (1996) Hydraulic conductivity of rock fractures. Transp Porous Media 23:1–30CrossRef

42.

Baheer I (2000) Selection of methodology for modeling hysteresis behavior of soils using neural networks. J Comput Aided Civ Infrastruct Eng 5(6):445–463

43.

Hecht-Nielsen R (1987) Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the first IEEE international conference on neural networks, San Diego, CA, pp 11–14

44.

Masters T (1994) Practical neural network recipes in C++. Academic Press, Boston

45.

Lippmann RP (1987) An introduction to computing with neural nets. IEEE ASSP Mag 4:4–22

46.

Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representation by error propogation. In: Rumelhart DE, McClelland JL (eds) Parallel distributed processing, vol 1, pp 318–362

47.

MacKay DJC (1992) Bayesian interpolation. Neural Comput 4:415–447CrossRef

48.

Hush DR (1989) Classification with neural networks: a performance analysis. Proceedings of the IEEE international conference on systems Engineering Dayton Ohia, USA, 277–80

49.

Ripley BD (1993) Statistical aspects of neural networks. In: Barndoff-Neilsen OE, Jensen JL, Kendall WS (eds) Networks and chaos-statistical and probabilistic aspects. Chapman & Hall, London, pp 40–123

50.

Wang C (1994) A theory of generalization in learning machines with neural application. PhD thesis, The University of Pennsylvania, USA

51.

Kanellopoulas I, Wilkinson GG (1997) Strategies and best practice for neural network image classification. Int J Remote Sensing 18:711–725CrossRef

Title: Artificial neural network for prediction of air flow in a single rock joint
Authors: P. G. Ranjith
Manoj Khandelwal
Publication date: 01-09-2012
Publisher: Springer-Verlag
Published in: Neural Computing and Applications / Issue 6/2012
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-011-0595-5

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 6/2012

Structure optimization of neural network for dynamic system modeling using multi-objective genetic algorithm

Pareto-optimal solutions based multi-objective particle swarm optimization control for batch processes

Direct adaptive robust NN control for a class of discrete-time nonlinear strict-feedback SISO systems

Leukocyte image segmentation by visual attention and extreme learning machine

A feature binding computational model for multi-class object categorization and recognition

A hybrid intelligent optimization method for multiple metal grades optimization

Premium Partner