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Optimized Support Vector Machines Combined with Evolutionary Random Forest for Prediction of Back-Break Caused by Blasting Operation

Author

Listed:
  • Qun Yu

    (School of Architecture and Civil Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Masoud Monjezi

    (Department of Mining, Faculty of Engineering, Tarbiat Modares University, Tehran 14115-143, Iran)

  • Ahmed Salih Mohammed

    (Civil Engineering Department, College of Engineering, University of Sulaimani, Sulaymaniyah 46001, Iraq)

  • Hesam Dehghani

    (Department of Mining Engineering, Hamedan University of Technology, Hamedan 6516913733, Iran)

  • Danial Jahed Armaghani

    (Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76, Lenin Prospect, 454080 Chelyabinsk, Russia)

  • Dmitrii Vladimirovich Ulrikh

    (Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76, Lenin Prospect, 454080 Chelyabinsk, Russia)

Abstract

Back-break is an adverse event in blasting works that causes the instability of mine walls, equipment collapsing, and reduction in effectiveness of drilling. Therefore, it boosts the total cost of mining operations. This investigation intends to develop optimized support vector machine models to forecast back-break caused by blasting. The Support Vector Machine (SVM) model was optimized using two advanced metaheuristic algorithms, including whale optimization algorithm (WOA) and moth–flame optimization (MFO). Before the models’ development, an evolutionary random forest (ERF) technique was used for input selection. This model selected five inputs out of 10 candidate inputs to be used to predict the back break. These two optimized SVM models were evaluated using various performance criteria. The performance of these two models was also compared with other hybridized SVM models. In addition, a sensitivity evaluation was made to find how the selected inputs influence the back-break magnitude. The outcomes of this study demonstrated that both the SVM–MFO and SVM–WOA improved the performance of the standard SVM. Additionally, the SVM–MFO showed a better performance than the SVM–WOA and other hybridized SVM models. The outcomes of this research recommend that the SVM–MFO can be considered as a powerful model to forecast the back-break induced by blasting.

Suggested Citation

  • Qun Yu & Masoud Monjezi & Ahmed Salih Mohammed & Hesam Dehghani & Danial Jahed Armaghani & Dmitrii Vladimirovich Ulrikh, 2021. "Optimized Support Vector Machines Combined with Evolutionary Random Forest for Prediction of Back-Break Caused by Blasting Operation," Sustainability, MDPI, vol. 13(22), pages 1-15, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:22:p:12797-:d:682916
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    References listed on IDEAS

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    1. Ahmed Salih Mohammed & Panagiotis G. Asteris & Mohammadreza Koopialipoor & Dimitrios E. Alexakis & Minas E. Lemonis & Danial Jahed Armaghani, 2021. "Stacking Ensemble Tree Models to Predict Energy Performance in Residential Buildings," Sustainability, MDPI, vol. 13(15), pages 1-22, July.
    2. Yan Li & Fathin Nur Syakirah Hishamuddin & Ahmed Salih Mohammed & Danial Jahed Armaghani & Dmitrii Vladimirovich Ulrikh & Ali Dehghanbanadaki & Aydin Azizi, 2021. "The Effects of Rock Index Tests on Prediction of Tensile Strength of Granitic Samples: A Neuro-Fuzzy Intelligent System," Sustainability, MDPI, vol. 13(19), pages 1-21, September.
    3. Danial Jahed Armaghani & Panagiotis G. Asteris & Behnam Askarian & Mahdi Hasanipanah & Reza Tarinejad & Van Van Huynh, 2020. "Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness," Sustainability, MDPI, vol. 12(6), pages 1-17, March.
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