Browse > Article
http://dx.doi.org/10.12989/gae.2022.30.6.489

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)
Publication Information
Geomechanics and Engineering / v.30, no.6, 2022 , pp. 489-502 More about this Journal
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
fly-rock; hybrid models; machine learning; metaheuristic optimization; sensitivity analysis;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 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.   DOI
2 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.   DOI
3 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.   DOI
4 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.   DOI
5 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.
6 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.
7 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.   DOI
8 Kennedy, J. and Eberhart, R. (1995), "Particle swarm optimization", Proceedings of ICNN'95-International Conference on Neural Networks, 4, 1942-1948, November.
9 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.   DOI
10 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.   DOI
11 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.   DOI
12 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.   DOI
13 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.   DOI
14 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.   DOI
15 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.   DOI
16 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.   DOI
17 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.   DOI
18 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.   DOI
19 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.   DOI
20 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.   DOI
21 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.   DOI
22 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.   DOI
23 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.   DOI
24 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.   DOI
25 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.   DOI
26 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.   DOI
27 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.   DOI
28 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.   DOI
29 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.   DOI
30 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.   DOI
31 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.   DOI
32 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.   DOI
33 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.   DOI
34 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.   DOI