Application of PSO to develop a powerful equation for prediction of flyrock due to blasting | Neural Computing and Applications
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Application of PSO to develop a powerful equation for prediction of flyrock due to blasting

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Abstract

Drilling and blasting is a widely-used method for rock fragmentation in open-pit mines, tunneling and civil projects. Flyrock, as one of the most dangerous effects induced by blasting, can cause substantial damage to structures and injury to human. Therefore, the ability to make proper predictions of flyrock distance is important to reduce and minimize the environmental side effects caused by blasting operation. The main goal of the present research is to develop a precise equation for predicting flyrock through particle swarm optimization (PSO) approach. For comparison purpose, multiple linear regression (MLR) was also used. In this regard, a database including several controllable blasting parameters was collected from 76 blasting events in three quarry sites, Malaysia. In modeling procedures, five effective parameters on the flyrock including burden, spacing, stemming, powder factor and rock density were used as input parameters, while flyrock was considered as output parameter. In order to check the performance of the developed models, several statistical functions, i.e., root-mean-square error, Nash and Sutcliffe and coefficient of multiple determination (R 2), were computed. The results revealed that the proposed PSO equation is more reliable than MLR in predicting the flyrock. Based on sensitivity analysis results, it was also found that the RD was the most effective parameter on the flyrock in the studied cases.

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Authors and Affiliations

  1. Department of Mining Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran

    Mahdi Hasanipanah

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

    Danial Jahed Armaghani

  3. School of Mining, College of Engineering, University of Tehran, Tehran, 11155-4563, Iran

    Hassan Bakhshandeh Amnieh

  4. UTM Construction Research Centre, Institute for Smart Infrastructure and Innovative Construction (ISIIC), Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Muhd Zaimi Abd Majid & Mahmood M. D. Tahir

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  1. Mahdi Hasanipanah
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  2. Danial Jahed Armaghani
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  4. Muhd Zaimi Abd Majid
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  5. Mahmood M. D. Tahir
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Correspondence to Mahdi Hasanipanah.

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Hasanipanah, M., Jahed Armaghani, D., Bakhshandeh Amnieh, H. et al. Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Comput & Applic 28 (Suppl 1), 1043–1050 (2017). https://doi.org/10.1007/s00521-016-2434-1

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