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ORELM: A Novel Machine Learning Approach for Prediction of Flyrock in Mine Blasting

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Abstract

Blast-induced flyrock is a hazardous and undesirable phenomenon that may occur in surface mines, especially when blasting takes place near residential areas. Therefore, accurate prediction of flyrock distance is of high significance in the determination of the statutory danger area. To this end, there is a practical need to propose an accurate model to predict flyrock. Aiming at this topic, this study presents two machine learning models, including extreme learning machine (ELM) and outlier robust ELM (ORELM), for predicting flyrock. To the best of our knowledge, this is the first work that investigates the use of ORELM model in the field of flyrock prediction. To construct and verify the proposed ELM and ORELM models, a database including 82 datasets has been collected from the three granite quarry sites in Malaysia. Additionally, artificial neural network (ANN) and multiple regression models were used for comparison. According to the results, both ELM and ORELM models performed satisfactorily, and their performances were far better compared to the performances of ANN and multiple regression models.

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Acknowledgment

This paper is supported by the National Key Research and Development Program of China (2016YFC0501103); the National Natural Science Foundation of China (Grant No. 51804299); and the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20180646).

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Authors and Affiliations

  1. School of Mines, China University of Mining and Technology, Xuzhou, 221116, China

    Xiang Lu

  2. State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou, 221116, China

    Xiang Lu

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

    Mahdi Hasanipanah

  4. Department of Mining Engineering, University of Kashan, Kashan, Iran

    Mahdi Hasanipanah

  5. Innovative Green Product Synthesis and Renewable Environment Development Research Group, Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Kathirvel Brindhadevi

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

    Hassan Bakhshandeh Amnieh

  7. Department of Construction Management, University of Houston, Houston, USA

    Seyedamirhesam Khalafi

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  1. Xiang Lu
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  2. Mahdi Hasanipanah
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  3. Kathirvel Brindhadevi
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  4. Hassan Bakhshandeh Amnieh
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  5. Seyedamirhesam Khalafi
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Correspondence to Mahdi Hasanipanah or Kathirvel Brindhadevi.

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Lu, X., Hasanipanah, M., Brindhadevi, K. et al. ORELM: A Novel Machine Learning Approach for Prediction of Flyrock in Mine Blasting. Nat Resour Res 29, 641–654 (2020). https://doi.org/10.1007/s11053-019-09532-2

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