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Blast-induced ground vibration prediction using support vector machine

- Khandelwal, Manoj

  • Authors: Khandelwal, Manoj
  • Date: 2011
  • Type: Text , Journal article
  • Relation: Engineering with Computers Vol. 27, no. 3 (2011), p. 193-200
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  • Description: Ground vibrations induced by blasting are one of the fundamental problems in the mining industry and may cause severe damage to structures and plants nearby. Therefore, a vibration control study plays an important role in the minimization of environmental effects of blasting in mines. In this paper, an attempt has been made to predict the peak particle velocity using support vector machine (SVM) by taking into consideration of maximum charge per delay and distance between blast face to monitoring point. To investigate the suitability of this approach, the predictions by SVM have been compared with conventional vibration predictor equations. Coefficient of determination (CoD) and mean absolute error were taken as a performance measure. © 2010 Springer-Verlag London Limited.

Application of soft computing to predict blast-induced ground vibration

- Khandelwal, Manoj, Kumar, Lalit, Yellishetty, Mohan

  • Authors: Khandelwal, Manoj , Kumar, Lalit , Yellishetty, Mohan
  • Date: 2011
  • Type: Text , Journal article
  • Relation: Engineering with Computers Vol. 27, no. 2 (2011), p. 117-125
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  • Description: 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. © 2009 Springer-Verlag London Limited.

A dimensional analysis approach to study blast-induced ground vibration

- Khandelwal, Manoj, Saadat, Mahdi

  • Authors: Khandelwal, Manoj , Saadat, Mahdi
  • Date: 2014
  • Type: Text , Journal article
  • Relation: Rock Mechanics and Rock Engineering Vol. 48, no. 2 (2014), p. 727-735
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  • Description: The prediction of ground vibration is of great importance in the alleviation of the detrimental effects of blasting. Therefore, a vibration control study to minimize the harm of ground vibration and its influence on nearby structures can play an important role in the mining industry. In this paper, a dimensional analysis (DA) technique has been performed on various blast design parameters to propose a new formula for the prediction of the peak particle velocity (PPV). After obtaining the DA formula, 105 data sets were used to determine the unknown coefficients of the DA equation, as well as site constants of different conventional predictor equations. Then, 12 new blast data sets were used to compare the capability of the DA formula with conventional predictor equations. The results were compared based on the coefficient of determination and mean absolute error between measured and predicted values of the PPV. © 2014, Springer-Verlag Wien.

Evaluation and assessment of blast-induced ground vibrations in an underground gold mine : a case study

- Tribe, Jarryd, Koroznikova, Larissa, Khandelwal, Manoj, Giri, Jason

  • Authors: Tribe, Jarryd , Koroznikova, Larissa , Khandelwal, Manoj , Giri, Jason
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Natural Resources Research Vol. 30, no. 6 (2021), p. 4673-4694
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  • Description: Ground vibrations induced during rock fragmentation by blasting remain a potential source of hazard for the stability of nearby structures. In this paper, to forecast the effect of blast-induced ground vibrations, dimensional analysis (DA) is proposed to predict peak particle velocity (PPV). In conventional predictor equations, the major and critical parameter for the estimation of PPV is square root scaled distance. The new formula based on DA was obtained considering various blast design parameters in order to improve the capability of PPV prediction. After obtaining the new DA equation for the prediction of PPV, 360 data sets were used to determine the unknown coefficients of the new equation as well as site constants of different conventional predictor equations. Then, ten additional randomly selected data sets were used to compare the capability of the new model with conventional predictor equations. The results were compared based on coefficient of determination (R2) and mean absolute error (MAE) between measured and predicted values of PPV. The proposed formula with the greatest R2 and the lowest MAE was the better option for predicting the PPV of induced vibrations for the measured field data. © 2021, International Association for Mathematical Geosciences.
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