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A SVR-GWO technique to minimize flyrock distance resulting from blasting

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Abstract

Flyrock is one of the most important environmental and hazardous issues in mine blasting, which can affect equipment and people, and may lead to fatal accidents. Therefore, prediction and minimization of this phenomenon are crucial objectives of many rock removal projects. This study is aimed to predict the flyrock distance with the use of machine learning techniques. The most effective parameters of flyrock were measured during blasting operations in six mines. In total, 262 data samples of blasting operations were accurately measured and used for approximation purposes. Then, flyrock was evaluated and estimated using three machine learning methods: principle component regression (PCR), support vector regression (SVR), and multivariate adaptive regression splines (MARS). Many models of PCR, SVR, and MARS were constructed for the flyrock distance prediction. The modeling process of each method is elaborated separately in a way to be used by other researchers. The most important parameters affecting these models were assessed to obtain the best performance for the developed models. Eventually, a preferable model of each machine learning technique was used for comparison purposes. According to the used performance indices, coefficient of determination (R2), and root mean square error, the SVR model showed a better performance capacity in predicting flyrock distance compared with the other proposed models. Thus, the SVR prediction model can be used to accurately predict flyrock distance, thereby properly determining the blast safety area. Additionally, the SVR model was optimized by new optimization algorithm namely gray wolf optimization (GWO) for minimizing the flyrock resulting from blasting operation. By developing optimization technique of GWO, the value of flyrock can be decreased 4% compared with the minimum flyrock distance.

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Acknowledgments

This research paper is made possible through the support of the Universiti Teknologi Malaysia (UTM) and the authors wish to appreciate their help and support.

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Authors and Affiliations

  1. Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia

    Danial Jahed Armaghani

  2. Faculty of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, 15914, Iran

    Mohammadreza Koopialipoor

  3. Department of Building Structures and Soil Engineering, Higher Technical School of Architecture, Universidad de Sevilla, 41012, Sevilla, Spain

    Maziyar Bahri

  4. Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam

    Mahdi Hasanipanah

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

    M. M. Tahir

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  1. Danial Jahed Armaghani
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  2. Mohammadreza Koopialipoor
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Correspondence to Mahdi Hasanipanah.

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Armaghani, D.J., Koopialipoor, M., Bahri, M. et al. A SVR-GWO technique to minimize flyrock distance resulting from blasting. Bull Eng Geol Environ 79, 4369–4385 (2020). https://doi.org/10.1007/s10064-020-01834-7

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