Artificial neural network for prediction and control of blasting vibrations in Assiut (Egypt) limestone quarry

Mohamed, M. T.

doi:10.1016/j.ijrmms.2008.06.004

Cited by 93 publications

(32 citation statements)

References 3 publications

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“…A strategy developed to avoid over-fitting or under-fitting is to investigate the goodness of prediction using different types of network architecture and select the network with minimum root mean square error (Hornik, 1991) and with the application of Bayesian regulation (MacKay, 1992) while constructing neural network. Again, neural networks with the higher number of input variables have better predictability (Mohamed, 2009). …”

Section: Ann Architecture In Blasting Problemsmentioning

confidence: 99%

Application of Artificial Neural Network for Blast Performance Evaluation

Trivedi¹

2014

IJRET

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Section: Ann Architecture In Blasting Problemsmentioning

confidence: 99%

Application of Artificial Neural Network for Blast Performance Evaluation

Trivedi¹

2014

IJRET

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“…Ground vibration is results of the blasting operation. Ground vibration induced due to blasting is very crucial and critical as compared to other ill effects due to involvement of public residing in the close vicinity of mining sites, regulating and ground vibration standards setting agencies together with mine owners, environmentalists and ecologists (Khandelwal and Singh 2005, 2006, 2007, 2009; Monjezi et al 2006). Also, with the emphasis shifting towards eco-friendly, sustainable and geo-environmental activities, the field of ground vibration have now become an important and imperative parameter for safe and smooth running of any mining and civil project.…”

Section: Introductionmentioning

confidence: 99%

“…Also, with the emphasis shifting towards eco-friendly, sustainable and geo-environmental activities, the field of ground vibration have now become an important and imperative parameter for safe and smooth running of any mining and civil project. When intensity of ground vibrations is high, it annoys human beings and even can damage nearby structures also (Mohamed 2009). So, measurement, control and prediction of ground vibrations levels are necessary to judge the design of the blasting operations.…”

Section: Introductionmentioning

confidence: 99%

“…Number of researchers (Yang and Zhang 1997;Cai and Zhao 1997;Maulenkamp and Grima 1999;Singh et al 2001;Khandelwal and Singh 2002, 2005, 2009Tawadrous 2006;Neaupane and Adhikari 2006;Monjezi and Dehghani 2008;Mohamed 2009;Khandelwal et al 2011) have used ANN in various geo-engineering problems and found very good results over other conventional and empirical techniques and methodology.…”

Section: Introductionmentioning

confidence: 99%

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Application of an Expert System for the Assessment of Blast Vibration

Khandelwal

2011

Geotech Geol Eng

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The purpose of this article is to evaluate and predict the blast induced ground vibration using different conventional vibration predictors and artificial neural network (ANN) at a surface coal mine of India. Ground Vibration is a seismic wave that spread out from the blast hole when detonated in a confined manner. 128 blast vibrations were recorded and monitored in and around the surface coal mine at different strategic and vulnerable locations. Among these, 103 blast vibrations data sets were used for the training of the ANN network as well as to determine site constants of various conventional vibration predictors, whereas rest 25 blast vibration data sets were used for the validation and comparison by ANN and empirical formulas. Two types of ANN model based on two parameters (maximum charge per delay and distance between blast face to monitoring point) and multiple parameters (burden, spacing, charge length, maximum charge per delay and distance between blast face to monitoring point) were used in the present study to predict the peak particle velocity. Finally, it is found that the ANN model based on multiple input parameters have better prediction capability over two input parameters ANN model and conventional vibration predictors.

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“…In recent years, some authors (Singh and Singh 2005;Khandelwal and Singh 2007;Singh 2004;Mohamed 2009) have attempted to develop new prediction models using ANN incorporating two or more parameters affecting the ground vibration. The authors found that the ANN approach seems to be a better option for better prediction of PPV.…”

Section: Introductionmentioning

confidence: 99%

Prediction of environmental impacts of quarry blasting operation using fuzzy logic

Fişne

Kuzu

Hudaverdi

2010

Environ Monit Assess

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Blast-induced ground vibration is one of the most important environmental impacts of blasting operations because it may cause severe damage to structures and plants in nearby environment. Estimation of ground vibration levels induced by blasting has vital importance for restricting the environmental effects of blasting operations. Several predictor equations have been proposed by various researchers to predict ground vibration prior to blasting, but these are site specific and not generally applicable beyond the specific conditions. In this study, an attempt has been made to predict the peak particle velocity (PPV) with the help of fuzzy logic approach using parameters of distance from blast face to vibration monitoring point and charge weight per delay. The PPV and charge weight per delay were recorded for 33 blast events at various distances and used for the validation of the proposed fuzzy model. The results of the fuzzy model were also compared with the values obtained from classical regression analysis. The root mean square error estimated for fuzzy-based model was 5.31, whereas it was 11.32 for classical regression-based model. Finally, the relationship between the measured and predicted values of PPV showed that the correlation coefficient for fuzzy model (0.96) is higher than that for regression model (0.82).

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Artificial neural network for prediction and control of blasting vibrations in Assiut (Egypt) limestone quarry

Cited by 93 publications

References 3 publications

Application of Artificial Neural Network for Blast Performance Evaluation

Application of Artificial Neural Network for Blast Performance Evaluation

Application of an Expert System for the Assessment of Blast Vibration

Prediction of environmental impacts of quarry blasting operation using fuzzy logic

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