首页|A new technique to predict fly-rock in bench blasting based on an ensemble of support vector regression and GLMNET

A new technique to predict fly-rock in bench blasting based on an ensemble of support vector regression and GLMNET

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  • NETL
  • NSTL
  • Springer Nature
Fly-rock caused by blasting is one of the dangerous side effects that need to be accurately predicted in open-pit mines. This study proposed a new technique to predict the distance of fly-rock based on an ensemble of support vector regression models (SVRs) and Lasso and elastic-net regularized generalized linear model (GLMNET), called SVRs-GLMNET. It was developed based on a combination of six SVR models and a GLMNET model. Accordingly, the dataset including 210 experimental data was divided into three parts, i.e., training, validating, and testing. Of the whole dataset, 70% was used for the development of the six SVR models first as the sub-models. Subsequently, 20% of the entire dataset (the validating dataset) was used to predict fly-rock based on the six developed SVR models. The predicted results from the six developed SVR models were used as the input variables to establish the GLMNET model (i.e., SVRs-GLMNET model). Finally, the remaining 10% of the dataset was used for testing the performance of the proposed SVRs-GLMNET model. A comparison and evaluation of the six developed SVR models and the proposed SVRs-GLMNET model were implemented based on five statistical criteria, such as mean absolute error (MAE), mean absolute percentage error (MAPE), root-mean-square error (RMSE), variance account for (VAF), and determination of correlation (R~2). The results indicated that the proposed SVRs-GLMNET model provided the most dominant performance in predicting the distance of fly-rock caused by bench blasting in this study with an RMSE of 3.737, R~2 of 0.993, MAE of 3.214, MAPE of 0.018, and VAF of 99.207. Whereas, the other models yielded poorer accuracy with RMSE of 7.058-12.779, R~2 of 0.920-0.972, MAE of 3.438-7.848, MAPE of 0.021-0.055, and VAF of 90.538-97.003.

Fly-rockSVRs-GLMNETBench blastingOpen-pit mineArtificial intelligence

Hongquan Guo、Hoang Nguyen、Xuan-Nam Bui、Danial Jahed Armaghani

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School of Resources and Safety Engineering, Central South University, Changsha 410083, Hunan, China

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

Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Vien Street, Duc Thang Ward, Bac Tu Liem District, Hanoi, Vietnam, Center for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, 18 Vien Street, Duc Thang Ward, Bac Tu Liem District, Hanoi, Vietnam

Faculty of Engineering, Centre of Tropical Geoengineering (GEOTROPIK), School of Civil Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia

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2021

10.1007/s00366-019-00833-x

Engineering with computers

Engineering with computers

ISSN：0177-0667
年,卷(期)：2021.37(1)
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