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Application of two intelligent systems in predicting environmental impacts of quarry blasting

Armaghani, Danial Jahed and Hassani, Mohsen and Monjezi, Masoud and Mohamad, Edy Tonnizam and Marto, Aminaton and Moghaddam, Mohammad Reza (2015) Application of two intelligent systems in predicting environmental impacts of quarry blasting. Arabian Journal Of Geosciences, 8 (11). pp. 9647-9665. ISSN 1866-7511

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Official URL: http://dx.doi.org/10.1007/s12517-015-1908-2


Blasting, as the most frequently used method for hard rock fragmentation, is a hazardous aspect in mining industries. These operations produce several undesirable environmental impacts such as ground vibration, air-overpressure (AOp), and flyrock in the nearby environments. These environmental impacts may cause injury to human and damage to structures, groundwater, and ecology of the nearby area. This paper is aimed to predict the blasting environmental impacts in granite quarry sites through two intelligent systems, namely artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). For this purpose, 166 blasting operations at four granite quarry sites in Malaysia were investigated and the values of peak particle velocity (PPV), AOp, and flyrock were precisely recorded in each blasting operation. Considering some model performance indices including coefficient of determination (R2), value account for (VAF), and root mean square error (RMSE), and also using simple ranking procedure, the best models for prediction of PPV, AOp, and flyrock were selected. The results demonstrated that the ANFIS models yield higher performance capacity compared to ANN models. In the case of testing datasets, the R2 values of 0.939, 0.947, and 0.959 for prediction of PPV, AOp, and flyrock, respectively, suggest the superiority of the ANFIS technique, while in predicting PPV, AOp, and flyrock using ANN technique, these values are 0.771, 0.864, and 0.834, respectively.

Item Type:Article
Uncontrolled Keywords:adaptive neuro-fuzzy inference system, air overpressure, artificial neural network, blasting environmental impacts, flyrock, peak particle velocity
Subjects:A General Works
ID Code:57880
Deposited By: Haliza Zainal
Deposited On:04 Dec 2016 12:07
Last Modified:26 Feb 2017 15:48

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