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Application of soft computing to predict blast-induced ground vibration

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Abstract

In this study, an attempt has been made to evaluate and predict the blast-induced ground vibration by incorporating explosive charge per delay and distance from the blast face to the monitoring point using artificial neural network (ANN) technique. A three-layer feed-forward back-propagation neural network with 2-5-1 architecture was trained and tested using 130 experimental and monitored blast records from the surface coal mines of Singareni Collieries Company Limited, Kothagudem, Andhra Pradesh, India. Twenty new blast data sets were used for the validation and comparison of the peak particle velocity (PPV) by ANN and conventional vibration predictors. Results were compared based on coefficient of determination and mean absolute error between monitored and predicted values of PPV.

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Authors and Affiliations

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

    Manoj Khandelwal

  2. Singareni Collieries Company Limited, Kothagudem, 507 101, Andhra Pradesh, India

    D. Lalit Kumar

  3. Department of Civil Engineering, Monash University, Melbourne, VIC, 3800, Australia

    Mohan Yellishetty

Authors
  1. Manoj Khandelwal
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  2. D. Lalit Kumar
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  3. Mohan Yellishetty
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Correspondence to Manoj Khandelwal.

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Khandelwal, M., Lalit Kumar, D. & Yellishetty, M. Application of soft computing to predict blast-induced ground vibration. Engineering with Computers 27, 117–125 (2011). https://doi.org/10.1007/s00366-009-0157-y

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  • DOI: https://doi.org/10.1007/s00366-009-0157-y

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