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A platform for research: civil engineering, architecture and urbanism
Prediction of Backbreak in Open-Pit Blasting Operations Using the Machine Learning Method
Abstract Backbreak is an undesirable phenomenon in blasting operations. It can cause instability of mine walls, falling down of machinery, improper fragmentation, reduced efficiency of drilling, etc. The existence of various effective parameters and their unknown relationships are the main reasons for inaccuracy of the empirical models. Presently, the application of new approaches such as artificial intelligence is highly recommended. In this paper, an attempt has been made to predict backbreak in blasting operations of Soungun iron mine, Iran, incorporating rock properties and blast design parameters using the support vector machine (SVM) method. To investigate the suitability of this approach, the predictions by SVM have been compared with multivariate regression analysis (MVRA). The coefficient of determination (CoD) and the mean absolute error (MAE) were taken as performance measures. It was found that the CoD between measured and predicted backbreak was 0.987 and 0.89 by SVM and MVRA, respectively, whereas the MAE was 0.29 and 1.07 by SVM and MVRA, respectively.
Prediction of Backbreak in Open-Pit Blasting Operations Using the Machine Learning Method
Abstract Backbreak is an undesirable phenomenon in blasting operations. It can cause instability of mine walls, falling down of machinery, improper fragmentation, reduced efficiency of drilling, etc. The existence of various effective parameters and their unknown relationships are the main reasons for inaccuracy of the empirical models. Presently, the application of new approaches such as artificial intelligence is highly recommended. In this paper, an attempt has been made to predict backbreak in blasting operations of Soungun iron mine, Iran, incorporating rock properties and blast design parameters using the support vector machine (SVM) method. To investigate the suitability of this approach, the predictions by SVM have been compared with multivariate regression analysis (MVRA). The coefficient of determination (CoD) and the mean absolute error (MAE) were taken as performance measures. It was found that the CoD between measured and predicted backbreak was 0.987 and 0.89 by SVM and MVRA, respectively, whereas the MAE was 0.29 and 1.07 by SVM and MVRA, respectively.
Prediction of Backbreak in Open-Pit Blasting Operations Using the Machine Learning Method
Khandelwal, Manoj (author) / Monjezi, M. (author)
2012
Article (Journal)
English
Local classification TIB:
560/4815/6545
BKL:
38.58
Geomechanik
/
56.20
Ingenieurgeologie, Bodenmechanik
Prediction of Backbreak in Open-Pit Blasting Operations Using the Machine Learning Method
| British Library Online Contents | 2013
Prediction of Backbreak in Open-Pit Blasting Operations Using the Machine Learning Method
| Online Contents | 2012
| British Library Online Contents | 2014