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An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine,Iran

Journal of Rock Mechanics and Geotechnical Engineering 2014年第1期6卷 67-76页

作者： Mahdi Saadat Manoj Khandelwal M.Monjezi Department of Mining Engineering Shahid Bahonar University of Kerman Department of Mining Engineering College of Technology&EngineeringMaharana Pratap University of Agriculture&Technology Faculty of Engineering Tarbiat Modares University

Blast-induced ground vibration is one of the inevitable outcomes of blasting in mining projects and may cause substantial damage to rock mass as well as nearby structures and human *** this paper,an attempt has been made to present an application of artificial neural network(ANN)to predict the blast-induced ground vibration of the Gol-E-Gohar(GEG)iron mine,Iran.A four-layer feed-forward back propagation multi-layer perceptron(MLP)was used and trained with Levenberg–Marquardt *** construct ANN models,the maximum charge per delay,distance from blasting face to monitoring point,stemming and hole depth were taken as inputs,whereas peak particle velocity(PPV)was considered as an output parameter.A database consisting of69data sets recorded at strategic and vulnerable locations of GEG iron mine was used to train and test the generalization capability of ANN *** of determination(R2)and mean square error(MSE)were chosen as the indicators of the performance of the networks.A network with architecture4-11-5-1and R2of0.957and MSE of0.000722was found to be *** demonstrate the supremacy of ANN approach,the same69data sets were used for the prediction of PPV with four common empirical models as well as multiple linear regression(MLR)*** results revealed that the proposed ANN approach performs better than empirical and MLR models.

关键词： Blast-induced ground vibration empirical predictors Artificial neural network(ANN) Multiple linear regression

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