Skip to main content
SpringerLink
Log in

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

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

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 (R2). 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, R2 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, R2 of 0.920–0.972, MAE of 3.438–7.848, MAPE of 0.021–0.055, and VAF of 90.538–97.003.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Source: https://www.lakecountrycalendar.com

Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Manoj K, Monjezi M (2013) Prediction of flyrock in open pit blasting operation using machine learning method. Int J Min Sci Technol 23(3):313–316

    Article  Google Scholar 

  2. Bajpayee T, Rehak T, Mowrey G, Ingram D (2004) Blasting injuries in surface mining with emphasis on flyrock and blast area security. J Saf Res 35(1):47–57

    Article  Google Scholar 

  3. Rehak T, Bajpayee T, Mowrey G, Ingram D (2001) Flyrock issues in blasting. In: Proc 27th Ann. Conf. Explos Blasting Tech, ISEE, The National Institute for Occupational Safety and Health (NIOSH), Cleveland, Ohio, pp 165–175

  4. Bui XN, Nguyen H, Le HA, Bui HB, Do NH (2019) Prediction of blast-induced air over-pressure in open-pit mine: assessment of different artificial intelligence techniques. Nat Resour Res. https://doi.org/10.1007/s11053-019-09461-0

    Article  Google Scholar 

  5. Nguyen H (2019) Support vector regression approach with different kernel functions for predicting blast-induced ground vibration: a case study in an open-pit coal mine of Vietnam. SN Appl Sci 1(4):283. https://doi.org/10.1007/s42452-019-0295-9

    Article  Google Scholar 

  6. Armaghani DJ, Hajihassani M, Mohamad ET, Marto A, Noorani S (2014) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab J Geosci 7(12):5383–5396

    Article  Google Scholar 

  7. Bahrami A, Monjezi M, Goshtasbi K, Ghazvinian A (2011) Prediction of rock fragmentation due to blasting using artificial neural network. Eng Comput 27(2):177–181

    Article  Google Scholar 

  8. Bakhshandeh Amnieh H, Jafari A (2017) Prediction of fragmentation due to blasting using mutual information and rock engineering system; case study: Meydook copper mine. Int J Min Geo-Eng 51(1):23–28

    Google Scholar 

  9. Dehghani H, Ataee-Pour M (2011) Development of a model to predict peak particle velocity in a blasting operation. Int J Rock Mech Min Sci 48(1):51–58

    Article  Google Scholar 

  10. Duan B, Xia H, Yang X (2018) Impacts of bench blasting vibration on the stability of the surrounding rock masses of roadways. Tunn Undergr Space Technol 71:605–622

    Article  Google Scholar 

  11. Nguyen H, Bui X-N, Bui H-B, Cuong DT (2019) Developing an XGBoost model to predict blast-induced peak particle velocity in an open-pit mine: a case study. Acta Geophys 67(2):477–490. https://doi.org/10.1007/s11600-019-00268-4

    Article  Google Scholar 

  12. Raina A, Chakraborty A, Ramulu M, Sahu P, Haldar A, Choudhury P (2004) Flyrock prediction and control in opencast mines: a critical appraisal. Min Eng J 6(5):10–20

    Google Scholar 

  13. Monjezi M, Bahrami A, Varjani AY, Sayadi AR (2011) Prediction and controlling of flyrock in blasting operation using artificial neural network. Arab J Geosci 4(3–4):421–425

    Article  Google Scholar 

  14. Amini H, Gholami R, Monjezi M, Torabi SR, Zadhesh J (2012) Evaluation of flyrock phenomenon due to blasting operation by support vector machine. Neural Comput Appl 21(8):2077–2085

    Article  Google Scholar 

  15. Bakhtavar E, Nourizadeh H, Sahebi A (2017) Toward predicting blast-induced flyrock: a hybrid dimensional analysis fuzzy inference system. Int J Environ Sci Technol 14(4):717–728

    Article  Google Scholar 

  16. Nguyen H, Bui X-N, Bui H-B, Mai N-L (2018) A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine, Vietnam. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3717-5

    Article  Google Scholar 

  17. Le LT, Nguyen H, Dou J, Zhou J (2019) A Comparative Study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in Estimating the Heating Load of Buildings’ Energy Efficiency for Smart City Planning. Appl Sci 9(13):2630

    Article  Google Scholar 

  18. Le LT, Nguyen H, Zhou J, Dou J, Moayedi H (2019) Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost. Appl Sci 9(13):2714

    Article  Google Scholar 

  19. Moayed H, Rashid ASA, Muazu MA, Nguyen H, Bui X-N, Bui DT (2019) Prediction of ultimate bearing capacity through various novel evolutionary and neural network models. Eng Comput. https://doi.org/10.1007/s00366-019-00723-2

    Article  Google Scholar 

  20. Nguyen H, Bui X-N (2018) Predicting blast-induced air overpressure: a robust artificial intelligence system based on artificial neural networks and random forest. Nat Resour Res. https://doi.org/10.1007/s11053-018-9424-1

    Article  Google Scholar 

  21. Nguyen H, Bui X-N, Moayedi H (2019) A comparison of advanced computational models and experimental techniques in predicting blast-induced ground vibration in open-pit coal mine. Acta Geophysica. https://doi.org/10.1007/s11600-019-00304-3

    Article  Google Scholar 

  22. Nguyen H, Bui X-N, Tran Q-H, Le T-Q, Do N-H, Hoa LTT (2018) Evaluating and predicting blast-induced ground vibration in open-cast mine using ANN: a case study in Vietnam. SN Appl Sci 1(1):125. https://doi.org/10.1007/s42452-018-0136-2

    Article  Google Scholar 

  23. Nguyen H, Bui X-N, Tran Q-H, Mai N-L (2019) A new soft computing model for estimating and controlling blast-produced ground vibration based on hierarchical K-means clustering and cubist algorithms. Appl Soft Comput 77:376–386. https://doi.org/10.1016/j.asoc.2019.01.042

    Article  Google Scholar 

  24. Nguyen H, Drebenstedt C, Bui X-N, Bui DT (2019) Prediction of blast-induced ground vibration in an open-pit mine by a novel hybrid model based on clustering and artificial neural network. Nat Resour Res. https://doi.org/10.1007/s11053-019-09470-z

    Article  Google Scholar 

  25. Nguyen H, Moayedi H, Foong LK, Al Najjar HAH, Jusoh WAW, Rashid ASA, Jamali J (2019) Optimizing ANN models with PSO for predicting short building seismic response. Eng Comput. https://doi.org/10.1007/s00366-019-00733-0

    Article  Google Scholar 

  26. Nguyen H, Moayedi H, Jusoh WAW, Sharifi A (2019) Proposing a novel predictive technique using M5Rules-PSO model estimating cooling load in energy-efficient building system. Eng Comput. https://doi.org/10.1007/s00366-019-00735-y

    Article  Google Scholar 

  27. Shang Y, Nguyen H, Bui X-N, Tran Q-H, Moayedi H (2019) A novel artificial intelligence approach to predict blast-induced ground vibration in open-pit mines based on the firefly algorithm and artificial neural network. Nat Resour Res. https://doi.org/10.1007/s11053-019-09503-7

    Article  Google Scholar 

  28. Wang B, Moayedi H, Nguyen H, Foong LK, Rashid ASA (2019) Feasibility of a novel predictive technique based on artificial neural network optimized with particle swarm optimization estimating pullout bearing capacity of helical piles. Eng Comput. https://doi.org/10.1007/s00366-019-00764-7

    Article  Google Scholar 

  29. Zhang X, Nguyen H, Bui X-N, Tran Q-H, Nguyen D-A, Bui DT, Moayedi H (2019) Novel soft computing model for predicting blast-induced ground vibration in open-pit mines based on particle swarm optimization and XGBoost. Nat Resour Res 1:1. https://doi.org/10.1007/s11053-019-09492-7

    Article  Google Scholar 

  30. Moayedi H, Hayati S (2018) Artificial intelligence design charts for predicting friction capacity of driven pile in clay. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3555-5

    Article  Google Scholar 

  31. Moayedi H, Armaghani DJ (2018) Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil. Eng Comput 34(2):347–356

    Article  Google Scholar 

  32. Moayedi H, Hayati S (2018) Applicability of a CPT-Based Neural Network Solution in Predicting Load-Settlement Responses of Bored Pile. Int J Geomech 18(6):06018009

    Article  Google Scholar 

  33. Rezaei M, Monjezi M, Varjani AY (2011) Development of a fuzzy model to predict flyrock in surface mining. Saf Sci 49(2):298–305

    Article  Google Scholar 

  34. Monjezi M, Mehrdanesh A, Malek A, Khandelwal M (2013) Evaluation of effect of blast design parameters on flyrock using artificial neural networks. Neural Comput Appl 23(2):349–356

    Article  Google Scholar 

  35. Marto A, Hajihassani M, Jahed Armaghani D, Tonnizam Mohamad E, Makhtar AM (2014) A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network. Sci World J 2014:643715. https://doi.org/10.1155/2014/643715

    Article  Google Scholar 

  36. Trivedi R, Singh T, Gupta N (2015) Prediction of blast-induced flyrock in opencast mines using ANN and ANFIS. Geotech Geol Eng 33(4):875–891

    Article  Google Scholar 

  37. Saghatforoush A, Monjezi M, Faradonbeh RS, Armaghani DJ (2016) Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting. Eng Comput 32(2):255–266

    Article  Google Scholar 

  38. Hasanipanah M, Armaghani DJ, Amnieh HB, Majid MZA, Tahir MM (2017) Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Comput Appl 28(1):1043–1050

    Article  Google Scholar 

  39. Faradonbeh RS, Armaghani DJ, Amnieh HB, Mohamad ET (2018) Prediction and minimization of blast-induced flyrock using gene expression programming and firefly algorithm. Neural Comput Appl 29(6):269–281

    Article  Google Scholar 

  40. Nikafshan Rad H, Bakhshayeshi I, Wan Jusoh WA, Tahir MM, Foong LK (2019) Prediction of flyrock in mine blasting: a new computational intelligence approach. Nat Resour Res. https://doi.org/10.1007/s11053-019-09464-x

    Article  Google Scholar 

  41. Guo H, Zhou J, Koopialipoor M, Jahed Armaghani D, Tahir MM (2019) Deep neural network and whale optimization algorithm to assess flyrock induced by blasting. Eng Comput. https://doi.org/10.1007/s00366-019-00816-y

    Article  Google Scholar 

  42. Asl PF, Monjezi M, Hamidi JK, Armaghani DJ (2018) Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm. Eng Comput 34(2):241–251. https://doi.org/10.1007/s00366-017-0535-9

    Article  Google Scholar 

  43. Zhou J, Aghili N, Ghaleini EN, Bui DT, Tahir MM, Koopialipoor M (2019) A Monte Carlo simulation approach for effective assessment of flyrock based on intelligent system of neural network. Eng Comput 1:1. https://doi.org/10.1007/s00366-019-00726-z

    Article  Google Scholar 

  44. Zhou J, Koopialipoor M, Murlidhar BR, Fatemi SA, Tahir MM, Jahed Armaghani D, Li C (2019) Use of intelligent methods to design effective pattern parameters of mine blasting to minimize flyrock distance. Nat Resour Res 1:1. https://doi.org/10.1007/s11053-019-09519-z

    Article  Google Scholar 

  45. Mohamad ET, Yi CS, Murlidhar BR, Saad R (2018) Effect of Geological Structure on Flyrock Prediction in Construction Blasting. Geotech Geol Eng 36(4):2217–2235. https://doi.org/10.1007/s10706-018-0457-3

    Article  Google Scholar 

  46. Hudaverdi T, Akyildiz O (2019) A new classification approach for prediction of flyrock throw in surface mines. Bull Eng Geol Env 78(1):177–187. https://doi.org/10.1007/s10064-017-1100-x

    Article  Google Scholar 

  47. Ghasemi E, Amini H, Ataei M, Khalokakaei R (2014) Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation. Arab J Geosci 7(1):193–202

    Article  Google Scholar 

  48. Shams S, Monjezi M, Majd VJ, Armaghani DJ (2015) Application of fuzzy inference system for prediction of rock fragmentation induced by blasting. Arab J Geosci 8(12):10819–10832

    Article  Google Scholar 

  49. Armaghani DJ, Mohamad ET, Hajihassani M, Abad SANK, Marto A, Moghaddam M (2016) Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods. Eng Comput 32(1):109–121

    Article  Google Scholar 

  50. Armaghani DJ, Mahdiyar A, Hasanipanah M, Faradonbeh RS, Khandelwal M, Amnieh HB (2016) Risk assessment and prediction of flyrock distance by combined multiple regression analysis and Monte Carlo simulation of quarry blasting. Rock Mech Rock Eng 49(9):3631–3641

    Article  Google Scholar 

  51. Koopialipoor M, Fallah A, Armaghani DJ, Azizi A, Mohamad ET (2019) Three hybrid intelligent models in estimating flyrock distance resulting from blasting. Eng Comput 35(1):243–256

    Article  Google Scholar 

  52. Tao T, Huang P, Wang S, Yi L (2018) Safety evaluation of blasting fly-rock based on unascertained measurement model. Instrum Mesure Metrol 17(1):55

    Google Scholar 

  53. Kalaivaani PT, Akila T, Tahir MM, Ahmed M, Surendar A (2019) A novel intelligent approach to simulate the blast-induced flyrock based on RFNN combined with PSO. Eng Comput. https://doi.org/10.1007/s00366-019-00707-2

    Article  Google Scholar 

  54. Rad HN, Hasanipanah M, Rezaei M, Eghlim AL (2018) Developing a least squares support vector machine for estimating the blast-induced flyrock. Eng Comput 34(4):709–717. https://doi.org/10.1007/s00366-017-0568-0

    Article  Google Scholar 

  55. Cortes C, Vapnik V (1995) Support vector machine. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  56. Basak D, Pal S, Patranabis DC (2007) Support vector regression. Neural Inf Process Lett Rev 11(10):203–224

    Google Scholar 

  57. Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33(1):1

    Article  Google Scholar 

  58. Hastie T, Qian J (2014) Glmnet vignette, pp 1–30. https://www.web.stanford.edu/~hastie/Papers/Glmnet_Vignette.pdf. Accessed 9 June 2016

  59. Dismuke C, Lindrooth R (2006) Ordinary least squares. Methods Des Outcomes Res 93:93–104

    Google Scholar 

  60. Hoerl AE, Kennard RW (1970) Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1):55–67

    Article  Google Scholar 

  61. Tibshirani R (1996) Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B (Methodol) 58:267–288

    MathSciNet  MATH  Google Scholar 

  62. Cawley GC (2006) Leave-one-out cross-validation based model selection criteria for weighted LS-SVMs. In: International joint conference on neural networks, 2006. IJCNN’06. 2006. IEEE, pp 1661–1668

  63. Güera D, Wang Y, Bondi L, Bestagini P, Tubaro S, Delp EJ (2017) A counter-forensic method for cnn-based camera model identification. In: 2017 IEEE conference on computer vision and pattern recognition workshops (CVPRW), 2017. IEEE, pp 1840–1847

  64. Knox SW (2018) Machine learning: a concise introduction, vol 285. Wiley, Hoboken

    Book  Google Scholar 

  65. Tien Bui D, Tran CT, Pradhan B, Revhaug I, Seidu R (2015) iGeoTrans–a novel iOS application for GPS positioning in geosciences. Geocarto Int 30(2):202–217

    Google Scholar 

  66. Sakia R (1992) The Box-Cox transformation technique: a review. Statistician 41:169–178

    Article  Google Scholar 

  67. Feizizadeh B, Roodposhti MS, Blaschke T, Aryal J (2017) Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping. Arab J Geosci 10(5):122

    Article  Google Scholar 

  68. Saltelli A, Annoni P, Azzini I, Campolongo F, Ratto M, Tarantola S (2010) Variance based sensitivity analysis of model output Design and estimator for the total sensitivity index. Comput Phys Commun 181(2):259–270

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Hanoi University of Mining and Geology (HUMG), Hanoi, Vietnam; Duy Tan University, Da Nang, Vietnam, and the Center for Mining, Electro-Mechanical research of HUMG.

Author information

Authors and Affiliations

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

    Hongquan Guo

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

    Hoang Nguyen

  3. Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Vien Street, Duc Thang Ward, Bac Tu Liem District, Hanoi, Vietnam

    Xuan-Nam Bui

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

    Xuan-Nam Bui

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

    Danial Jahed Armaghani

Authors
  1. Hongquan Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hoang Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xuan-Nam Bui
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Danial Jahed Armaghani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hoang Nguyen.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, H., Nguyen, H., Bui, XN. et al. A new technique to predict fly-rock in bench blasting based on an ensemble of support vector regression and GLMNET. Engineering with Computers 37, 421–435 (2021). https://doi.org/10.1007/s00366-019-00833-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-019-00833-x

Keywords

Search

Navigation