In this paper, we used artificial intelligence (AI) techniques to investigate the relation between the rock size distribution (RSD) and blasting parameters for rock fragmentation in quarries. Moreover, the ant colony optimization (ACO)–boosted regression tree (BRT) model, which is a novel AI technique for predicting RSD using the blasting parameters, is proposed based on the ACO and BRT algorithms. For predicting RSD, three well-developed models, namely the particle swarm optimization–adaptive neuro-fuzzy inference system (PSO–ANFIS), firefly algorithm (FFA)–ANFIS, and FFA–artificial neural network, were applied to the same dataset. Additionally, four benchmark AI techniques, i.e., support vector machine, k-nearest neighbors, principal component regression, and Gaussian process, and a conventional approach, i.e., the Kuz–Ram model, were employed for considering and predicting RSD. Using an image processing technique, the Split-Desktop software package was used to analyze the RSD of 136 blasting events at a quarry in Vietnam. Results were used as inputs, such as powder factor, explosive charge per delay, bench height, stemming length, and burden, and outputs, i.e., RSD, in this study. The novel scoring and color-intensity methods were used for visualizing several statistical criteria, including the correlation coefficient, mean absolute error, and root-mean-square error, to evaluate the model performance. Results indicate that the proposed ACO–BRT hybrid model yields higher RSD predictive accuracy than that obtained using any other model. The proposed model seems to be promising for optimizing the blasting parameters to increase the production efficiency while reducing the production costs.
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Armaghani, D. J., Hajihassani, M., Monjezi, M., Mohamad, E. T., Marto, A., & Moghaddam, M. R. (2015). Application of two intelligent systems in predicting environmental impacts of quarry blasting. Arabian Journal of Geosciences,8(11), 9647–9665.
Arthur, C. K., Temeng, V. A., & Ziggah, Y. Y. (2019). Novel approach to predicting blast-induced ground vibration using Gaussian process regression. Engineering with Computers. https://doi.org/10.1007/s00366-018-0686-3.
Asl, P. F., Monjezi, M., Hamidi, J. K., & Armaghani, D. J. (2018). Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm. Engineering with Computers,34(2), 241–251.
Asteris, P. G., & Nikoo, M. (2019). Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures. Neural Computing and Applications. https://doi.org/10.1007/s00521-018-03965-1.
AyalaCarcedo, F. (2017). Drilling and blasting of rocks. Routledge. 1351454617.
Babaeian, M., Ataei, M., Sereshki, F., Sotoudeh, F., & Mohammadi, S. (2019). A new framework for evaluation of rock fragmentation in open pit mines. Journal of Rock Mechanics and Geotechnical Engineering,11(2), 325–336.
Bahadori, M., & Bakhshandeh Amnieh, H. (2018). Implementation of hyperbolic tangent function to estimate size distribution of rock fragmentation by blasting in open pit mines. International Journal of Mining and Geo-Engineering,52(2), 187–197.
Bahrami, A., Monjezi, M., Goshtasbi, K., & Ghazvinian, A. (2011). Prediction of rock fragmentation due to blasting using artificial neural network. Engineering with Computers,27(2), 177–181.
Box, G. E., & Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society: Series B (Methodological),26(2), 211–243.
Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. CRC Press. 0412048418.
Bui, X. N., Nguyen, H., Le, H. A., Bui, H. B., & Do, N. H. (2019). Prediction of blast-induced air over-pressure in open-pit mine: Assessment of different artificial intelligence techniques. Natural Resources Research. https://doi.org/10.1007/s11053-019-09461-0.
Cunningham, C. (1983). The Kuz–Ram Model for production of fragmentation from blasting. In Proceedings of first Symposium on Rock Fragmentation by Blasting, Lulea.
Cunningham, C. (1987). Fragmentation estimations and the Kuz–Ram model-four years on. In Proceedings of the Second International Symposium on Rock Fragmentation by Blasting.
Dershowitz, N., & Nissan, E. (2014). Language, culture, computation: computing-theory and technology: Essays dedicated to Yaacov Choueka on the occasion of his 75 birthday. Springer. 364245321X.
Dorigo, M., & Birattari, M. (2010). Ant colony optimization. Springer. 0387307680.
Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics,26(1), 29–41.
Dorigo, M., & Stützle, T. (2019). Ant colony optimization: Overview and recent advances (pp. 311–351). Handbook of metaheuristics: Springer.
Ebrahimi, E., Monjezi, M., Khalesi, M. R., & Armaghani, D. J. (2016). Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm. Bulletin of Engineering Geology and the Environment,75(1), 27–36. https://doi.org/10.1007/s10064-015-0720-2.
Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology,77(4), 802–813.
Enayatollahi, I., Bazzazi, A. A., & Asadi, A. (2014). Comparison between neural networks and multiple regression analysis to predict rock fragmentation in open-pit mines. Rock Mechanics and Rock Engineering,47(2), 799–807.
Fang, Q., Nguyen, H., Bui, X.-N., & Nguyen-Thoi, T. (2019a). Prediction of blast-induced ground vibration in open-pit mines using a new technique based on imperialist competitive algorithm and M5Rules. Natural Resources Research. https://doi.org/10.1007/s11053-019-09577-3.
Fang, Q., Nguyen, H., Bui, X.-N., & Tran, Q.-H. (2019b). Estimation of blast-induced air overpressure in quarry mines using cubist-based genetic algorithm. Natural Resources Research. https://doi.org/10.1007/s11053-019-09575-5.
Faramarzi, F., Mansouri, H., & Farsangi, M. E. (2013). A rock engineering systems based model to predict rock fragmentation by blasting. International Journal of Rock Mechanics and Mining Sciences,60, 82–94.
Folchi, R. (2003). Environmental impact statement for mining with explosives: A quantitative method. In Proceedings of the annual conference on explosives and blasting technique. ISEE; 1999.
Ghomsheh, V. S., Shoorehdeli, M. A., & Teshnehlab, M. (2007). Training ANFIS structure with modified PSO algorithm. In 2007 Mediterranean Conference on Control & Automation. IEEE.
Ghosh, A., Daemen, J., & Van Zyl, D. (1990). Fractal-based approach to determine the effect of discontinuities on blast fragmentation. In The 31th US Symposium on Rock Mechanics (USRMS). American Rock Mechanics Association.
Guo, H., Nguyen, H., Bui, X.-N., & Armaghani, D. J. (2019a). A new technique to predict fly-rock in bench blasting based on an ensemble of support vector regression and GLMNET. Engineering with Computers. https://doi.org/10.1007/s00366-019-00833-x.
Guo, H., Nguyen, H., Vu, D.-A., & Bui, X.-N. (2019b). Forecasting mining capital cost for open-pit mining projects based on artificial neural network approach. Resources Policy. https://doi.org/10.1016/j.resourpol.2019.101474.
Hasanipanah, M., Amnieh, H. B., Arab, H., & Zamzam, M. S. (2018). Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Computing and Applications,30(4), 1015–1024.
Hekmat, A., Munoz, S., & Gomez, R. (2019). Prediction of rock fragmentation based on a modified Kuz–Ram model. In Proceedings of the 27th international symposium on mine planning and equipment selection-MPES 2018. Springer.
Hjelmberg, H. (1983). Some ideas on how to improve calculations of the fragment size distribution in bench blasting. In 1st international symposium on rock fragmentation by blasting. Lulea University Technology Lulea Sweden.
Hustrulid, W. (1999). Blasting principles for open pit mining: Volume 2—Theoretical foundations. AA Balkema. 9054104600.
Jang, J.-S. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics,23(3), 665–685.
Jolliffe, I. (2011). Principal component analysis. Springer. 3642048978.
Kahriman, A., Ozer, U., Aksoy, M., Karadogan, A., & Tuncer, G. (2006). Environmental impacts of bench blasting at Hisarcik Boron open pit mine in Turkey. Environmental Geology,50(7), 1015–1023.
Kou, S., & Rustan, A. (1993). Computerized design and result prediction of bench blasting. In Proceedings of the first international symposium on rock fragmentation by blasting, HP Rossmanith ed.
Kulatilake, P., Qiong, W., Hudaverdi, T., & Kuzu, C. (2010). Mean particle size prediction in rock blast fragmentation using neural networks. Engineering Geology,114(3–4), 298–311.
Kuznetsov, V. (1973). The mean diameter of the fragments formed by blasting rock. Journal of Mining Science,9(2), 144–148.
Le, L. T., 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. Applied Sciences,9(13), 2630.
Liu, L., Moayedi, H., Rashid, A. S. A., Rahman, S. S. A., & Nguyen, H. (2019). Optimizing an ANN model with genetic algorithm (GA) predicting load-settlement behaviours of eco-friendly raft-pile foundation (ERP) system. Engineering with Computers. https://doi.org/10.1007/s00366-019-00767-4.
Lownds, C. (1995). Prediction of fragmentation based on distribution of explosives energy. Cleveland, OH: International Society of Explosives Engineers.
Moayed, H., Rashid, A. S. A., Muazu, M. A., Nguyen, H., Bui, X.-N., & Bui, D. T. (2019). Prediction of ultimate bearing capacity through various novel evolutionary and neural network models. Engineering with Computers. https://doi.org/10.1007/s00366-019-00723-2.
Mojtahedi, S. F. F., Ebtehaj, I., Hasanipanah, M., Bonakdari, H., & Amnieh, H. B. (2019). Proposing a novel hybrid intelligent model for the simulation of particle size distribution resulting from blasting. Engineering with Computers,35(1), 47–56.
Monjezi, M., Rezaei, M., & Varjani, A. Y. (2009a). Prediction of rock fragmentation due to blasting in Gol-E-Gohar iron mine using fuzzy logic. International Journal of Rock Mechanics and Mining Sciences,46(8), 1273–1280.
Monjezi, M., Shahriar, K., Dehghani, H., & Namin, F. S. (2009b). Environmental impact assessment of open pit mining in Iran. Environmental Geology,58(1), 205–216.
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 Applied Sciences,1(4), 283. https://doi.org/10.1007/s42452-019-0295-9.
Nguyen, H., Bui, X.-N., Bui, H.-B., & Cuong, D. T. (2019a). Developing an XGBoost model to predict blast-induced peak particle velocity in an open-pit mine: A case study. Acta Geophysica,67(2), 477–490. https://doi.org/10.1007/s11600-019-00268-4.
Nguyen, H., Bui, X.-N., Bui, H.-B., & Mai, N.-L. (2018a). A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine, Vietnam. Neural Computing and Applications. https://doi.org/10.1007/s00521-018-3717-5.
Nguyen, H., Bui, X.-N., & Moayedi, H. (2019b). 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.
Nguyen, H., Bui, X.-N., Tran, Q.-H., Le, T.-Q., Do, N.-H., & Hoa, L. T. T. (2018b). Evaluating and predicting blast-induced ground vibration in open-cast mine using ANN: A case study in Vietnam. SN Applied Sciences,1(1), 125. https://doi.org/10.1007/s42452-018-0136-2.
Nguyen, H., Bui, X.-N., Tran, Q.-H., & Mai, N.-L. (2019c). A new soft computing model for estimating and controlling blast-produced ground vibration based on hierarchical K-means clustering and cubist algorithms. Applied Soft Computing,77, 376–386. https://doi.org/10.1016/j.asoc.2019.01.042.
Nguyen, H., Bui, X.-N., Tran, Q.-H., & Moayedi, H. (2019d). Predicting blast-induced peak particle velocity using BGAMs, ANN and SVM: A case study at the Nui Beo open-pit coal mine in Vietnam. Environmental Earth Sciences,78(15), 479. https://doi.org/10.1007/s12665-019-8491-x.
Nguyen, H., Drebenstedt, C., Bui, X.-N., & Bui, D. T. (2019e). Prediction of blast-induced ground vibration in an open-pit mine by a novel hybrid model based on clustering and artificial neural network. Natural Resources Research. https://doi.org/10.1007/s11053-019-09470-z.
Nguyen, H., Moayedi, H., Foong, L. K., Al Najjar, H. A. H., Jusoh, W. A. W., Rashid, A. S. A., et al. (2019f). Optimizing ANN models with PSO for predicting short building seismic response. Engineering with Computers. https://doi.org/10.1007/s00366-019-00733-0.
Nguyen, H., Moayedi, H., Jusoh, W. A. W., & Sharifi, A. (2019g). Proposing a novel predictive technique using M5Rules-PSO model estimating cooling load in energy-efficient building system. Engineering with Computers. https://doi.org/10.1007/s00366-019-00735-y.
Nick, N. (2008). Joseph Juran, 103, pioneer in quality control, dies. New York Times,3, 3.
Otterness, R., Stagg, M., Rholl, S., & Smith, N. (1991). Correlation of shot design parameters to fragmentation. In Proceedings of 7th annual research symposium on explosives and blasting technique. ISEE, Solon.
Potts, G., & Ouchterlony, F. (2005). The capacity of image analysis to measure fragmentation: An evaluation using Split Desktop. Swedish Blasting Research Centre och Luleå tekniska universitet.
Pousinho, H. M. I., Mendes, V. M. F., & da Silva Catalão, J. P. (2011). A hybrid PSO–ANFIS approach for short-term wind power prediction in Portugal. Energy Conversion and Management,52(1), 397–402.
Razmjooy, N., Khalilpour, M., & Ramezani, M. (2016). A new meta-heuristic optimization algorithm inspired by FIFA world cup competitions: Theory and its application in PID designing for AVR system. Journal of Control, Automation and Electrical Systems,27(4), 419–440.
Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., & Tarantola, S. (2010). Variance based ssensitivity analysis of model output. Design and estimator for the total sensitivity index. Computer Physics Communications,181(2), 259–270.
Schapire, R. E. (2003). The boosting approach to machine learning: An overview. Nonlinear estimation and classification (pp. 149–171). Berlin: Springer.
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. Natural Resources Research. https://doi.org/10.1007/s11053-019-09503-7.
Shayanfar, H., & Gharehchopogh, F. S. (2018). Farmland fertility: A new metaheuristic algorithm for solving continuous optimization problems. Applied Soft Computing,71, 728–746.
Shi, X., Zhou, J., Wu, B.-B., Huang, D., & Wei, W. (2012). Support vector machines approach to mean particle size of rock fragmentation due to bench blasting prediction. Transactions of Nonferrous Metals Society of China, 22(2), 432–441.
Singh, D., & Sastry, V. (1986). Influence of structural discontinuity on rock fragmentation by blasting. In Proceedings of the 6th international symposium on intense dynamic loading and its effects. Beijing.
Swingler, K. (1996). Applying neural networks: A practical guide. Morgan Kaufmann. 0126791708.
Tarantola, S., Gatelli, D., Kucherenko, S., & Mauntz, W. (2007). Estimating the approximation error when fixing unessential factors in global sensitivity analysis. Reliability Engineering & System Safety,92(7), 957–960.
van Gerven, M., & Bohte, S. (2018). Artificial neural networks as models of neural information processing. Frontiers Media SA. 2889454010.
Wang, L. S.-L. (2010). Intelligent soft computation and evolving data mining: Integrating advanced technologies: integrating advanced technologies. IGI Global. 1615207589.
Wang, B., Moayedi, H., Nguyen, H., Foong, L. K., & Rashid, A. S. A. (2019). Feasibility of a novel predictive technique based on artificial neural network optimized with particle swarm optimization estimating pullout bearing capacity of helical piles. Engineering with Computers. https://doi.org/10.1007/s00366-019-00764-7.
Yaghoobi, H., Mansouri, H., Farsangi, M. A. E., & Nezamabadi-Pour, H. (2019). Determining the fragmented rock size distribution using textural feature extraction of images. Powder Technology,342, 630–641.
Zhang, X., Nguyen, H., Bui, X.-N., Tran, Q.-H., Nguyen, D.-A., Bui, D. T., et al. (2019). Novel soft computing model for predicting blast-induced ground vibration in open-pit mines based on particle swarm optimization and XGBoost. Natural Resources Research. https://doi.org/10.1007/s11053-019-09492-7.
Zhang, S., & Yin, S. (2014). Determination of in situ stresses and elastic parameters from hydraulic fracturing tests by geomechanics modeling and soft computing. Journal of Petroleum Science and Engineering, 124, 484–492.
Zhou, J., Li, C., Arslan, C. A., Hasanipanah, M., & Bakhshandeh Amnieh, H. (2019a). Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting. Engineering with Computers. https://doi.org/10.1007/s00366-019-00822-0.
Zhou, J., Li, E., Yang, S., Wang, M., Shi, X., Yao, S., & Mitri, H. S. (2019b). Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories. Safety Science, 118, 505–518.
Zhu, Z., Mohanty, B., & Xie, H. (2007). Numerical investigation of blasting-induced crack initiation and propagation in rocks. International Journal of Rock Mechanics and Mining Sciences,44(3), 412–424.
Zhu, Z., Xie, H., & Mohanty, B. (2008). Numerical investigation of blasting-induced damage in cylindrical rocks. International Journal of Rock Mechanics and Mining Sciences,45(2), 111–121.
This research is partially supported by the Training Project for Young Scholar of Institutions of High Education of Henan Province (2018GGJS122), Natural Science Foundation of Henan Province (182300410160), Henan Science and Technology Research Planning Project (182102310804), and Anyang Science and Technology Research Planning Project (AK66). The authors also would like to thank Hanoi University of Mining and Geology (HUMG), Hanoi, Vietnam, and the Center for Mining, Electro-Mechanical Research of HUMG; Duy Tan University, Da Nang, Vietnam, and Ton Duc Thang University, Ho Chi Minh City, Vietnam, for supporting this research.
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Zhang, S., Bui, XN., Trung, NT. et al. Prediction of Rock Size Distribution in Mine Bench Blasting Using a Novel Ant Colony Optimization-Based Boosted Regression Tree Technique. Nat Resour Res 29, 867–886 (2020). https://doi.org/10.1007/s11053-019-09603-4
- Rock fragmentation
- Rock size distribution
- Bench blasting
- Ant colony optimization
- Artificial intelligence