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

Qun Yu, Masoud Monjezi, Ahmed Salih Mohammed, Hesam Dehghani, Danial Jahed Armaghani and Dmitrii Vladimirovich Ulrikh
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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

Sustainability, 2021, vol. 13, issue 22, 1-15

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.

Keywords: blasting; back-break; SVM; metaheuristic algorithms; moth–flame optimization; whale optimization algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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