An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine, Iran
- Authors: Saadat, Mahdi , Khandelwal, Manoj , Monjezi, Masoud
- Date: 2014
- Type: Text , Journal article
- Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 6, no. 1 (2014), p. 67-76
- Full Text: false
- Reviewed:
- Description: 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 beings. In 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 algorithm. To 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 of 69 data sets recorded at strategic and vulnerable locations of GEG iron mine was used to train and test the generalization capability of ANN models. Coefficient of determination (R2) and mean square error (MSE) were chosen as the indicators of the performance of the networks. A network with architecture 4-11-5-1 and R2 of 0.957 and MSE of 0.000722 was found to be optimum. To demonstrate the supremacy of ANN approach, the same 69 data sets were used for the prediction of PPV with four common empirical models as well as multiple linear regression (MLR) analysis. The results revealed that the proposed ANN approach performs better than empirical and MLR models. © 2013 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences.
A dimensional analysis approach to study blast-induced ground vibration
- 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
- Full Text: false
- Reviewed:
- 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.