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Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on Particle Swarm Optimization and XGBoost

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Abstract

Blasting is a useful technique for rocks fragmentation in open-pit mines, underground mines, as well as for civil engineering work. However, the negative impacts of blasting, especially ground vibration, on the surrounding environment are significant. Ground vibration spreads to rocky environment and is characterized by peak particle velocity (PPV). At high PPV intensity, structures can be damaged and cause instability of slope. Therefore, accurately predict PPV is needed to protect the structures and slope stability. In this research, a novel intelligent approach for predicting blast-induced PPV was developed. The particle swarm optimization (PSO) and extreme gradient boosting machine (XGBoost) were applied to obtain the goal, called the PSO-XGBoost model. Accordingly, the PSO algorithm was used for optimization of hyper-parameters of XGBoost. A variety of empirical models were also considered and applied for comparison of the proposed PSO-XGBoost model. Accuracy criteria including mean absolute error, determination coefficient (R2), variance account for, and root-mean-square error were used for the assessment of models. For this study, 175 blasting operations were analyzed. The results showed that the proposed PSO-XGBoost emerged as the most reliable model. In contrast, the empirical models yielded worst performances.

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References

  • Abdel-Rasoul, E. I. (2000). Measurement and analysis of the effect of ground vibrations induced by blasting at the limestone quarries of the Egyptian cement company. Egypt: Cairo University.

    Google Scholar 

  • Agrawal, A. P., & Kaur, A. (2018). A comprehensive comparison of ant colony and hybrid particle swarm optimization algorithms through test case selection. In S. Satapathy, V. Bhateja, K. Raju, & B. Janakiramaiah (Eds.), Data engineering and intelligent computing (pp. 397–405). Berlin: Springer.

    Chapter  Google Scholar 

  • Alnaqi, A. A., Moayedi, H., Shahsavar, A., & Nguyen, T. K. (2019). Prediction of energetic performance of a building integrated photovoltaic/thermal system thorough artificial neural network and hybrid particle swarm optimization models. Energy Conversion and Management,183, 137–148.

    Article  Google Scholar 

  • Ambraseys, N. R., & Hendron, A. J. (1968). Dynamic behavior of rock masses. In: K. G. Stagg & O. C. Zienkiewicz (Eds.), Rock mechanics in engineering practices (pp. 203–207). New York: Wiley.

    Google Scholar 

  • Amnieh, H. B., Siamaki, A., & Soltani, S. (2012). Design of blasting pattern in proportion to the peak particle velocity (PPV): Artificial neural networks approach. Safety Science,50(9), 1913–1916.

    Article  Google Scholar 

  • Armaghani, D. J., Hasanipanah, M., Amnieh, H. B., & Mohamad, E. T. (2018). Feasibility of ICA in approximating ground vibration resulting from mine blasting. Neural Computing and Applications,29(9), 457–465.

    Article  Google Scholar 

  • Badal, K. (2010). Blast vibration studies in surface mines. Rourkela: National Institute of Technology.

    Google Scholar 

  • Behzadafshar, K., Mohebbi, F., Soltani Tehrani, M., Hasanipanah, M., & Tabrizi, O. (2018). Predicting the ground vibration induced by mine blasting using imperialist competitive algorithm. Engineering Computations,35(4), 1774–1787.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Carranza, E. J. M. (2017). Natural resources research publications on geochemical anomaly and mineral potential mapping, and introduction to the special issue of papers in these fields. Natural Resources Research,26(4), 379–410. https://doi.org/10.1007/s11053-017-9348-1.

    Article  Google Scholar 

  • Carranza, E. J. M., & Laborte, A. G. (2016). Data-driven predictive modeling of mineral prospectivity using random forests: A case study in Catanduanes Island (Philippines). Natural Resources Research,25(1), 35–50. https://doi.org/10.1007/s11053-015-9268-x.

    Article  Google Scholar 

  • Chatterjee, S., Sarkar, S., Hore, S., Dey, N., Ashour, A. S., & Balas, V. E. (2017). Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings. Neural Computing and Applications,28(8), 2005–2016.

    Article  Google Scholar 

  • Chen, T., & He, T. (2015). XGBoost: Extreme gradient boosting. R package version 0.4-2.

  • Clerc, M., & Kennedy, J. (2002). The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation,6(1), 58–73.

    Article  Google Scholar 

  • Duvall, W. I., & Fogelson, D. E. (1962). Review of criteria for estimating damage to residences from blasting vibrations. Bureau of Mines: US Department of the Interior.

    Google Scholar 

  • Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In MHS’95. Proceedings of the sixth international symposium on micro machine and human science, 1995 (pp. 39–43). IEEE.

  • Eberhart, R. C., & Shi, Y. (2000). Comparing inertia weights and constriction factors in particle swarm optimization. In Proceedings of the 2000 congress on evolutionary computation, 2000 (Vol. 1, pp. 84–88). IEEE.

  • Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.

    Article  Google Scholar 

  • Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics and Data Analysis,38(4), 367–378.

    Article  Google Scholar 

  • Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). The Annals of Statistics,28(2), 337–407.

    Article  Google Scholar 

  • Gao, W., Dimitrov, D., & Abdo, H. (2018a). Tight independent set neighborhood union condition for fractional critical deleted graphs and ID deleted graphs. Discrete and Continuous Dynamical Systems-S,17, 123–144.

    Google Scholar 

  • Gao, W., Guirao, J. L., Basavanagoud, B., & Wu, J. (2018b). Partial multi-dividing ontology learning algorithm. Information Sciences,467, 35–58.

    Article  Google Scholar 

  • Ghezelbash, R., Maghsoudi, A., & Carranza, E. J. M. (2019). An improved data-driven multiple criteria decision-making procedure for spatial modeling of mineral Prospectivity: Adaption of prediction—area plot and logistic functions. Natural Resources Research,1, 1–9. https://doi.org/10.1007/s11053-018-9448-6.

    Article  Google Scholar 

  • Gutowski, T. G., & Dym, C. L. (1976). Propagation of ground vibration: A review. Journal of Sound and Vibration,49(2), 179–193.

    Article  Google Scholar 

  • Hajihassani, M., Armaghani, D. J., Marto, A., & Mohamad, E. T. (2015). Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bulletin of Engineering Geology and the Environment,74(3), 873–886.

    Article  Google Scholar 

  • Hasanipanah, M., Golzar, S. B., Larki, I. A., Maryaki, M. Y., & Ghahremanians, T. (2017). Estimation of blast-induced ground vibration through a soft computing framework. Engineering with Computers,33(4), 951–959.

    Article  Google Scholar 

  • Janahiraman, T. V., Ahmad, N., & Nordin, F. H. (2018). Extreme learning machine and particle swarm optimization in optimizing CNC turning operation. In IOP conference series: Materials science and engineering, 2018 (Vol. 1, 342, p. 012086): IOP Publishing.

  • Jayasinghe, L., Zhao, Z., Goh, A., Zhou, H., Gui, Y., & Tao, M. (2018). A field study on pile response to blast-induced ground motion. Soil Dynamics and Earthquake Engineering,114, 568–575.

    Article  Google Scholar 

  • Kennedy, J. (1998). The behavior of particles. In V. W. Porto, N. Saravanan, D. Waagen, & A. E. Eiben (Eds.), International conference on evolutionary programming (pp. 579–589). Berlin, Heidelberg: Springer.

    Google Scholar 

  • Kennedy, J. (2010). Particle swarm optimization. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of machine learning (pp. 760–766). Boston, MA: Springer.

    Google Scholar 

  • Kumar, R., Choudhury, D., & Bhargava, K. (2013). Prediction of blast-induced vibration parameters for soil sites. International Journal of Geomechanics,14(3), 04014007.

    Article  Google Scholar 

  • Kumar, R., Choudhury, D., & Bhargava, K. (2016). Determination of blast-induced ground vibration equations for rocks using mechanical and geological properties. Journal of Rock Mechanics and Geotechnical Engineering,8(3), 341–349.

    Article  Google Scholar 

  • Langefors, U., & Kihlström, B. (1963). The modern technique of rock blasting. New York: Wiley.

    Google Scholar 

  • Marinakis, Y., Migdalas, A., & Sifaleras, A. (2017). A hybrid particle swarm optimization—variable neighborhood search algorithm for constrained shortest path problems. European Journal of Operational Research,261(3), 819–834.

    Article  Google Scholar 

  • Mines, U. S. B. O., & Siskind, D. (1980). Structure response and damage produced by ground vibration from surface mine blasting. Bureau of Mines New York: US Department of the Interior.

    Google Scholar 

  • Moayedi, & Hayati, S. (2018a). Applicability of a CPT-based neural network solution in predicting load-settlement responses of bored pile. International Journal of Geomechanics,18(6), 06018009.

    Article  Google Scholar 

  • Moayedi, H., & Hayati, S. (2018b). Artificial intelligence design charts for predicting friction capacity of driven pile in clay. Neural Computing and Applications. https://doi.org/10.1007/s00521-018-3555-5.

    Article  Google Scholar 

  • Moayedi, H., Moatamediyan, A., Nguyen, H., Bui, X.-N., Bui, D. T., & Rashid, A. S. A. (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.

    Article  Google Scholar 

  • Murlidhar, B. R., Armaghani, D. J., Mohamad, E. T., & Changthan, S. (2018). Rock fragmentation prediction through a new hybrid model based on imperial competitive algorithm and neural network. Smart Construction Research, 2(3), 1–12. https://doi.org/10.18063/scr.v2i3.397.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Nguyen, H., & Bui, X.-N. (2018). Predicting blast-induced air overpressure: A robust artificial intelligence system based on artificial neural networks and random forest. Natural Resources Research. https://doi.org/10.1007/s11053-018-9424-1.

    Article  Google Scholar 

  • Nguyen, H., Bui, X.-N., Bui, H.-B., & Cuong, D. (2019a). Developing a XGBoost model to predict blast-induced peak particle velocity in open cast mine: A case study. Acta Geophysica. https://doi.org/10.1007/s11600-019-00268-4.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Nguyen, H., Bui, X.-N., Tran, Q.-H., & Mai, N.-L. (2019b). 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,1, 1–20. https://doi.org/10.1016/j.asoc.2019.01.042.

    Article  Google Scholar 

  • Nguyen, H., Drebenstedt, C., Bui, X.-N., & Bui, D. T. (2019c). 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.

    Article  Google Scholar 

  • Nguyen, H., Moayedi, H., Foong, L. K., Al Najjar, H. A. H., Jusoh, W. A. W., Rashid, A. S. A., et al. (2019d). Optimizing ANN models with PSO for predicting short building seismic response. Engineering with Computers. https://doi.org/10.1007/s00366-019-00733-0.

    Article  Google Scholar 

  • Nourani, V., Elkiran, G., Abdullahi, J., & Tahsin, A. (2019). Multi-region modeling of daily global solar radiation with artificial intelligence ensemble. Natural Resources Research. https://doi.org/10.1007/s11053-018-09450-9.

    Article  Google Scholar 

  • Protodyakonov, M. (1962). Mechanical properties and drillability of rocks. In Proceedings of the fifth symposium on rock mechanics, 1962 (pp. 103–118). Minneapolis, MN: University of Minnesota.

  • Rad, N. H., Bakhshayeshi, I., Jusoh, W. A., Tahir, M. M., & Foong, L. K. (2019). Prediction of Flyrock in mine blasting: A new computational intelligence approach. Natural Resources Research. https://doi.org/10.1007/s11053-019-09464-x.

    Article  Google Scholar 

  • Saadat, M., Khandelwal, M., & Monjezi, M. (2014). An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine, Iran. Journal of Rock Mechanics and Geotechnical Engineering,6(1), 67–76.

    Article  Google Scholar 

  • Shi, Y. (2001). Particle swarm optimization: developments, applications and resources. In Proceedings of the 2001 congress on evolutionary computation, 2001 (Vol. 1, pp. 81–86). IEEE.

  • Singh, A., & Biswas, A. (2016). Application of global particle swarm optimization for inversion of residual gravity anomalies over geological bodies with idealized geometries. Natural Resources Research,25(3), 297–314. https://doi.org/10.1007/s11053-015-9285-9.

    Article  Google Scholar 

  • Taheri, K., Hasanipanah, M., Golzar, S. B., & Majid, M. Z. A. (2017). A hybrid artificial bee colony algorithm-artificial neural network for forecasting the blast-produced ground vibration. Engineering with Computers,33(3), 689–700.

    Article  Google Scholar 

  • Verma, A., & Singh, T. (2013). Comparative study of cognitive systems for ground vibration measurements. Neural Computing and Applications,22(1), 341–350.

    Article  Google Scholar 

  • Yousefi, M., & Carranza, E. J. M. (2016). Data-driven index overlay and boolean logic mineral Prospectivity modeling in Greenfields exploration. Natural Resources Research,25(1), 3–18. https://doi.org/10.1007/s11053-014-9261-9.

    Article  Google Scholar 

  • Zhou, J., Li, E., Wang, M., Chen, X., Shi, X., & Jiang, L. (2019). Feasibility of stochastic gradient boosting approach for evaluating seismic liquefaction potential based on SPT and CPT case histories. Journal of Performance of Constructed Facilities,33(3), 04019024.

    Article  Google Scholar 

  • Zhou, J., Li, X., & Mitri, H. S. (2016). Classification of rockburst in underground projects: Comparison of ten supervised learning methods. Journal of Computing in Civil Engineering,30(5), 04016003.

    Article  Google Scholar 

  • Zhou, J., Li, X., & Shi, X. (2012). Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines. Safety Science,50(4), 629–644.

    Article  Google Scholar 

  • Zhou, J., Yang, J., Lin, L., Zhu, Z., & Ji, Z. (2018). Local best particle swarm optimization using crown jewel defense strategy. In In Y. Shi (Ed.), Critical developments and applications of swarm intelligence (pp. 27–52). Pennsylvania: IGI Global.

    Chapter  Google Scholar 

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Acknowledgments

The authors need to thank Hanoi University of Mining and Geology (HUMG); Ministry of Education and Training of Vietnam (MOET) and the Center for Mining, Electro-Mechanical research of HUMG for supporting this research.

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Authors and Affiliations

  1. CAS Key Laboratory of Mechanical Behavior and Design of Materials, University of Science and Technology of China, Hefei, 230026, Anhui, China

    Xiliang Zhang

  2. State Key Laboratory of Safety and Health for Metal Mines, Sinosteel Maanshan Institute of Mining Research, Co., Ltd., Maanshan, 243000, Anhui, China

    Xiliang Zhang

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

    Hoang Nguyen

  4. Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Pho Vien, Duc Thang ward, Bac Tu Liem District, Hanoi, Vietnam

    Xuan-Nam Bui, Quang-Hieu Tran & Dinh-An Nguyen

  5. Center for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, 18 Pho Vien, Duc Thang ward, Bac Tu Liem District, Hanoi, Vietnam

    Xuan-Nam Bui & Quang-Hieu Tran

  6. GIS Group, Department of Business and IT, University of South-Eastern Norway, Gullbringvegen 36, 3800, Bø i Telemark, Norway

    Dieu Tien Bui

  7. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Hossein Moayedi

  8. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Hossein Moayedi

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  1. Xiliang Zhang
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  2. Hoang Nguyen
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  3. Xuan-Nam Bui
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  6. Dieu Tien Bui
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  7. Hossein Moayedi
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Correspondence to Hoang Nguyen or Hossein Moayedi.

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Zhang, X., Nguyen, H., Bui, XN. et al. Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on Particle Swarm Optimization and XGBoost. Nat Resour Res 29, 711–721 (2020). https://doi.org/10.1007/s11053-019-09492-7

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