Geomechanics and Engineering

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Meta-heuristic optimization algorithms for prediction of fly-rock in the blasting operation of open-pit mines

  • Mahmoodzadeh, Arsalan (Rock Mechanics Division, School of Engineering, Tarbiat Modares University) ;
  • Nejati, Hamid Reza (Rock Mechanics Division, School of Engineering, Tarbiat Modares University) ;
  • Mohammadi, Mokhtar (Department of Information Technology, College of Engineering and Computer Science, Lebanese French University) ;
  • Ibrahim, Hawkar Hashim (Department of Civil Engineering, College of Engineering, Salahaddin University-Erbil) ;
  • Rashidi, Shima (Department of Computer Science, College of Science and Technology, University of Human Development) ;
  • Mohammed, Adil Hussein (Department of Communication and Computer Engineering, Faculty of Engineering, Cihan University-Erbil)
  • Received : 2022.04.07
  • Accepted : 2022.08.14
  • Published : 2022.09.25
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Abstract

In this study, a Gaussian process regression (GPR) model as well as six GPR-based metaheuristic optimization models, including GPR-PSO, GPR-GWO, GPR-MVO, GPR-MFO, GPR-SCA, and GPR-SSO, were developed to predict fly-rock distance in the blasting operation of open pit mines. These models included GPR-SCA, GPR-SSO, GPR-MVO, and GPR. In the models that were obtained from the Soungun copper mine in Iran, a total of 300 datasets were used. These datasets included six input parameters and one output parameter (fly-rock). In order to conduct the assessment of the prediction outcomes, many statistical evaluation indices were used. In the end, it was determined that the performance prediction of the ML models to predict the fly-rock from high to low is GPR-PSO, GPR-GWO, GPR-MVO, GPR-MFO, GPR-SCA, GPR-SSO, and GPR with ranking scores of 66, 60, 54, 46, 43, 38, and 30 (for 5-fold method), respectively. These scores correspond in conclusion, the GPR-PSO model generated the most accurate findings, hence it was suggested that this model be used to forecast the fly-rock. In addition, the mutual information test, also known as MIT, was used in order to investigate the influence that each input parameter had on the fly-rock. In the end, it was determined that the stemming (T) parameter was the most effective of all the parameters on the fly-rock.

Keywords

References

  1. Amini, H., Gholami, R., Monjezi, M., Torabi, S.R. and Zadhesh, J. (2012), "Evaluation of fly-rock phenomenon due to blasting operation by support vector machine", Neur. Comput. Appl., 21, 2077-2085. https://doi.org/10.1007/s00521-011-0631-5.
  2. Bai, X., Cheng, W.C., Ong, D.E.L. and Li, G. (2021), "Evaluation of geological conditions and clogging of tunneling using machine learning", Geomech. Eng., 25(1), 59-73. https://doi.org/10.12989/gae.2021.25.1.059.
  3. Deng, Z., Hu, X., Lin, X., Che, Y., Xu, L. and Guo, W. (2020), "Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression", Energy, 205, 118000. https://doi.org/10.1016/j.energy.2020.118000.
  4. Faradonbeh, R.S., Jahed Armaghani, D., Amnieh, H.B. and Mohamad, E.T. (2018), "Prediction and minimization of blast-induced fly-rock using gene expression programming and firefly algorithm", Neur. Comput. Appl., 29, 269-281. https://doi.org/10.1007/s00521-016-2537-8.
  5. Fattahi, H. and Hasanipanah, M. (2021), "An integrated approach of ANFIS-grasshopper optimization algorithm to approximate fly-rock distance in mine blasting", Eng. Comput., 38(3), 2619-2631. https://doi.org/10.1007/s00366-020-01231-4.
  6. Guo, H., Nguyen, H., Bui, X.N. and Jahed Armaghani, D. (2021a), "A new technique to predict fly-rock in bench blasting based on an ensemble of support vector regression and GLMNET", Eng. Comput., 37, 421-435. https://doi.org/10.1007/s00366-019-00833-x.
  7. Guo, H., Zhou, J., Koopialipoor, M., Jahed Armaghani, D. and Tahir, M.M. (2021b), "Deep neural network and whale optimization algorithm to assess flyrock induced by blasting", Eng. Comput., 37, 173-186. https://doi.org/10.1007/s00366-019-00816-y.
  8. Han, H., Jahed Armaghani, D., Tarinejad, R., Zhou, J. and Tahir, M.M. (2020), "Random forest and bayesian network techniques for probabilistic prediction of fly-rock induced by blasting in quarry sites", Nat. Resour. Res., 29, 655-667. https://doi.org/10.1007/s11053-019-09611-4.
  9. Harandizadeh, H. and Jahed Armaghani, D. (2021), "Prediction of air-overpressure induced by blasting using an ANFIS-PNN model optimized by GA", Appl. Soft Comput., 99, 106904. https://doi.org/10.1016/j.asoc.2020.106904.
  10. Hasanipanah, M., Keshtegar, B., Thai, D.K. and Troung, N.T. (2020), "An ANN-adaptive dynamical harmony search algorithm to approximate the fly-rock resulting from blasting", Eng. Comput., 1-13. https://doi.org/10.1007/s00366-020-01105-9.
  11. James, J.Q. and Li, V.O.K. (2015), "A social spider algorithm for global optimization", Appl. Soft Comput., 30, 614-627. https://doi.org/10.1016/j.asoc.2015.02.014.
  12. Kennedy, J. and Eberhart, R. (1995), "Particle swarm optimization", Proceedings of ICNN'95-International Conference on Neural Networks, 4, 1942-1948, November.
  13. Khandelwal, M. and Monjezi, M. (2013), "Prediction of fly-rock in open pit blasting operation using machine learning method", Int. J. Min. Sci. Technol., 23(3), 313-316. https://doi.org/10.1016/j.ijmst.2013.05.005.
  14. Kumar, S., Mishra, A.K. and Choudhary, B.S. (2021), "Prediction of back break in blasting using random decision trees", Eng. Comput., 38(2), 1185-1191. https://doi.org/10.1007/s00366-020-01280-9.
  15. Li, B., Fu, Y., Hong, Y. and Gao, Z (2021), "Deterministic and probabilistic analysis of tunnel face stability using support vector machine", Geomech. Eng., 25(1), 17-30. https://doi.org/10.12989/gae.2021.25.1.017.
  16. Li, X., Yuan, C. and Wang, Z. (2020), "Multi-time-scale framework for prognostic health condition of lithium battery using modified Gaussian process regression and nonlinear regression", J. Power Sour., 467, 228358. https://doi.org/10.1016/j.jpowsour.2020.228358.
  17. Liu, J., Jiang, Y., Zhang, Y. and Sakaguchi, O. (2021), "Influence of different combinations of measurement while drilling parameters by artificial neural network on estimation of tunnel support patterns", Geomech. Eng., 25(6), 439-454. https://doi.org/10.12989/gae.2021.25.6.439.
  18. Liu, S., Tai, H., Ding, Q., Li, D., Xu, L. and Wei, Y. (2013), "A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction", Math. Comput. Model., 58, 458-465. https://doi.org/10.1016/j.mcm.2011.11.021.
  19. Mahmoodzadeh, A., Rashidi, S., Mohammed, A., Hama Ali, H. and Ibrahim, H. (2022), "Machine learning approaches to enable resource forecasting process of road tunnels construction", Communication Engineering and Computer Science, North America, March. Available at: . Date accessed: 26 Sep. 2022.
  20. Mahmoodzadeh, A., Mohammadi, M., Ibrahim, H.H., Abdulhamid, S.N., Ham-Ali, H.F., Hasan, A.M., Khishe, M. and Mahmud, H. (2021a), "Machine learning forecasting models of disc cutters life of tunnel boring machine", Autom. Constr., 128, 103779. https://doi.org/10.1016/j.autcon.2021.103779.
  21. Mahmoodzadeh, A., Mohammadi, M., Ibrahim, H.H., Noori, K.M.G., Abdulhamid, S.N. and Hama-Ali, H.F. (2021c), "Forecasting sidewall displacement of underground caverns using machine learning techniques", Autom. Constr., 123, 103530. https://doi.org/10.1016/j.autcon.2020.103530.
  22. Mahmoodzadeh, A., Mohammadi, M., Noori, K.M.G., Khishe, M., Ibrahim, H.H., Hama-Ali, H.F. and Abdulhamid, S.N. (2021b), "Presenting the best prediction model of water inflow into drill and blast tunnels among several machine learning techniques", Autom. Constr., 127, 103719. https://doi.org/10.1016/j.autcon.2021.103719.
  23. Mirjalili, S. (2015), "Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm", Knowled.-Bas. Syst., 89, 228-249. https://doi.org/10.1016/j.knosys.2015.07.006.
  24. Mirjalili, S. (2016), "SCA: A Sine Cosine Algorithm for solving optimization problems", Knowled.-Bas. Syst., 96, 120-133. https://doi.org/10.1016/j.knosys.2015.12.022.
  25. Mirjalili, S., Mirjalili, S.M. and Hatamlou, A. (2016), "Multi-verse optimizer: A nature-inspired algorithm for global optimization", Neur. Comput. Appl., 27, 495-513. https://doi.org/10.1007/s00521-015-1870-7.
  26. Mirjalili, S., Mirjalili, S.M. and Lewis, A. (2014), "Grey wolf optimizer", Adv. Eng. Softw., 69, 46-61 https://doi.org/10.1016/j.advengsoft.2013.12.007.
  27. Momeni, E., Dowlatshahi, M.B., Omidinasab, F., Maizir, H. and Jahed Armaghani, D. (2020), "Gaussian process regression technique to estimate the pile bearing capacity", Arab. J. Sci. Eng., 45, 8255-8267. https://doi.org/10.1007/s13369-020-04683-4.
  28. Murlidhar, B.R., Kumar, D., Jahed Armaghani, D., Mohamad, E.T., Roy, B. and Pham, B.T. (2020), "A Novel Intelligent ELM-BBO Technique for Predicting Distance of Mine Blasting-Induced Fly-rock", Nat. Resour. Res., 29, 4103-4120. https://doi.org/10.1007/s11053-020-09676-6.
  29. Peng, Y., Su, Y., Wu, L. and Chen, C. (2019), "Study on the attenuation characteristics of seismic wave energy induced by underwater drilling and blasting", Shock Vib., 2019, Article ID 4367698. https://doi.org/10.1155/2019/4367698.
  30. Taghavi, M. and Khishe, M. (2019), "A modified grey wolf optimizer by individual best memory and penalty factor for sonar and radar dataset classification", Iran. J. Marine Technol., 6(1), 122-132.
  31. Verron, S., Tiplica, T. and Kobi, A. (2008), "Fault detection and identification with a new feature selection based on mutual information", J. Proc. Control, 18, 479-490. https://doi.org/10.1016/j.jprocont.2007.08.003.
  32. Xiang, G., Ying, D., Gao, C. and Yuan, L. (2021), "Application of artificial neural network for prediction of flow ability of soft soil subjected to vibrations", Geomech. Eng., 25(5), 395-403. https://doi.org/10.12989/gae.2021.25.5.395.
  33. Ye, J., Koopialipoor, M., Zhou, J., Jahed Armaghani, D. and He, X. (2021), "A novel combination of tree-based modeling and monte carlo simulation for assessing risk levels of fly-rock induced by mine blasting", Nat. Resour. Res., 30, 225-243. https://doi.org/10.1007/s11053-020-09730-3.
  34. Zhang, Y. and Xu, X. (2020), "Curie temperature modeling of magnetocaloric lanthanum manganites using Gaussian process regression", J. Magnet. Magnetic Mater., 512, 166998. https://doi.org/10.1016/j.jmmm.2020.166998.