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

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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.

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Authors and Affiliations

  1. Department of Civil Engineering, Monash University, Clayton, VIC, 3800, Australia

    P. G. Ranjith

  2. Department of Mining Engineering, College of Technology and Engineering, Maharana Pratap University of Agriculture and Technology, Udaipur, 313 001, India

    Manoj Khandelwal

Authors
  1. P. G. Ranjith
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  2. Manoj Khandelwal
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Correspondence to Manoj Khandelwal.

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Ranjith, P.G., Khandelwal, M. Artificial neural network for prediction of air flow in a single rock joint. Neural Comput & Applic 21, 1413–1422 (2012). https://doi.org/10.1007/s00521-011-0595-5

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  • DOI: https://doi.org/10.1007/s00521-011-0595-5

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