Application of two intelligent systems in predicting environmental impacts of quarry blasting
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ORIGINAL PAPER
Application of two intelligent systems in predicting environmental impacts of quarry blasting Danial Jahed Armaghani 1 & Mohsen Hajihassani 2 & Masoud Monjezi 3 & Edy Tonnizam Mohamad 1 & Aminaton Marto 1 & Mohammad Reza Moghaddam 4,5
Received: 24 December 2014 / Accepted: 1 April 2015 # Saudi Society for Geosciences 2015
Abstract 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
* Masoud Monjezi [email protected] Danial Jahed Armaghani [email protected]
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. Keywords Blasting environmental impacts . Peak particle velocity . Air overpressure . Flyrock . Artificial neural network . Adaptive neuro-fuzzy inference system
Mohsen Hajihassani [email protected] Edy Tonnizam Mohamad [email protected]
Introduction
Aminaton Marto [email protected]
Blasting is a common technique of rock fragmentation in quarry and mining operations as well as some civil engineering applications such as tunneling and road construction. In quarry operations, blasting consists of drilling several rows of blast-holes almost parallel to the free face of the bench. These operations create several environmental impacts such as air overpressure, ground vibration, flyrock, and back-break around the blasting zone (Monjezi and Dehghani 2008; Fisne et al. 2011; Jahed Armaghani et al. 2013; Hajihassani et al. 2014a; Ebrahimi et al. 2015). There are some empirical equations for prediction of these environmental impacts. Nevertheless, these equations just consider limited numbers of influential parameters on them whereas these impacts are
Mohammad Reza Moghaddam [email protected] 1
Department of Geotechnic
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