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

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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 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 (R 2), 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 R 2 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.

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Acknowledgments

The authors would like to extend their appreciation to the Universiti Teknologi Malaysia for UTM Research University Grant No. 01H88 and provide required facilities that made this research possible.

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Authors and Affiliations

  1. Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, UTM Skudai, Johor, Malaysia

    Danial Jahed Armaghani, Edy Tonnizam Mohamad & Aminaton Marto

  2. Construction Research Alliance, Universiti Teknologi Malaysia, 81310, UTM Skudai, Johor, Malaysia

    Mohsen Hajihassani

  3. Department of Mining, Tarbiat Modares University, Tehran, 14115-143, Iran

    Masoud Monjezi

  4. South Tehran Branch, Islamic Azad University, Tehran, Iran

    Mohammad Reza Moghaddam

  5. Saman Zamin Hamgam Engineering Company, Tehran, Iran

    Mohammad Reza Moghaddam

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  1. Danial Jahed Armaghani
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  2. Mohsen Hajihassani
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  3. Masoud Monjezi
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  4. Edy Tonnizam Mohamad
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  5. Aminaton Marto
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  6. Mohammad Reza Moghaddam
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Correspondence to Masoud Monjezi.

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Jahed Armaghani, D., Hajihassani, M., Monjezi, M. et al. Application of two intelligent systems in predicting environmental impacts of quarry blasting. Arab J Geosci 8, 9647–9665 (2015). https://doi.org/10.1007/s12517-015-1908-2

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