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Prediction of blast-induced air overpressure: a hybrid AI-based predictive model

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Abstract

Blast operations in the vicinity of residential areas usually produce significant environmental problems which may cause severe damage to the nearby areas. Blast-induced air overpressure (AOp) is one of the most important environmental impacts of blast operations which needs to be predicted to minimize the potential risk of damage. This paper presents an artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) for the prediction of AOp induced by quarry blasting. For this purpose, 95 blasting operations were precisely monitored in a granite quarry site in Malaysia and AOp values were recorded in each operation. Furthermore, the most influential parameters on AOp, including the maximum charge per delay and the distance between the blast-face and monitoring point, were measured and used to train the ICA-ANN model. Based on the generalized predictor equation and considering the measured data from the granite quarry site, a new empirical equation was developed to predict AOp. For comparison purposes, conventional ANN models were developed and compared with the ICA-ANN results. The results demonstrated that the proposed ICA-ANN model is able to predict blast-induced AOp more accurately than other presented techniques.

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Acknowledgments

The authors would like to extend their appreciation to the Ministry of Science, Technology and Innovation, Malaysia through the provision of research grant No: 03-01-06-SF1185 (UTM 4S072) to undertake the research. Acknowledgement is also conveyed to the Universiti Teknologi Malaysia for providing the required facilities which 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 Bahru, Johor, Malaysia

    Danial Jahed Armaghani

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

    Mohsen Hajihassani

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

    Aminaton Marto

  4. Young Researchers and Elit Club, South Tehran Branch, Islamic Azad University, Tehran, Iran

    Roohollah Shirani Faradonbeh

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

    Edy Tonnizam Mohamad

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  1. Danial Jahed Armaghani
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  2. Mohsen Hajihassani
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  4. Roohollah Shirani Faradonbeh
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  5. Edy Tonnizam Mohamad
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Correspondence to Danial Jahed Armaghani.

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Jahed Armaghani, D., Hajihassani, M., Marto, A. et al. Prediction of blast-induced air overpressure: a hybrid AI-based predictive model. Environ Monit Assess 187, 666 (2015). https://doi.org/10.1007/s10661-015-4895-6

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