Skip to main content
SpringerLink
Log in

Prediction of Blast-Induced Ground Vibration in Open-Pit Mines Using a New Technique Based on Imperialist Competitive Algorithm and M5Rules

  • Original Paper
  • Published:
Natural Resources Research Aims and scope Submit manuscript

Abstract

In this paper, blast-induced ground vibration (BIGV) was considered as the primary objective, and a new artificial intelligence system was proposed to predict BIGV with high accuracy based on the M5Rules and imperialist competitive algorithm (ICA), called ICA–M5Rules technique. Accordingly, the ICA was considered to optimize the M5Rules based on the rules of the M5 model, as well as the prune and smooth procedures. To evaluate the effectiveness of the proposed ICA–M5Rules technique, random forest (RF), classical M5Rules, and support vector machine (SVM) were developed as the benchmark techniques to compare with the proposed ICA–M5Rules technique. Besides, two existing empirical equations were each used to develop models based on the experimental datasets to estimate BIGV for comparison with the proposed ICA–M5Rules model. A case study of a quarry mine in Vietnam was adopted for the developed (ICA–M5Rules, M5Rules, RF, SVM, empirical) models based on 125 blasting events. Mean absolute error, root-mean-squared error, and determination of coefficient (R2) were applied and computed to evaluate the accuracy, as well as the performance of the developed models. The findings indicated that the proposed ICA–M5Rules model provided highest accuracy. The primary objective was appropriately addressed based on the obtained results of the developed models. As well established, the proposed ICA–M5Rules model was introduced as a new system to predict BIGV in open-pit mines accurately.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17

Similar content being viewed by others

References

  • Ambraseys, N. (1968). Rock mechanics in engineering practice. New York: Wiley.

    Google Scholar 

  • Amiri, M., Ghiasi-Freez, J., Golkar, B., & Hatampour, A. (2015). Improving water saturation estimation in a tight shaly sandstone reservoir using artificial neural network optimized by imperialist competitive algorithm—A case study. Journal of Petroleum Science and Engineering,127, 347–358.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Armaghani, D. J., Hasanipanah, M., Mahdiyar, A., Majid, M. Z. A., Amnieh, H. B., & Tahir, M. M. (2018b). Airblast prediction through a hybrid genetic algorithm-ANN model. Neural Computing and Applications,29(9), 619–629.

    Article  Google Scholar 

  • Armaghani, D. J., Hajihassani, M., Sohaei, H., Mohamad, E. T., Marto, A., Motaghedi, H., et al. (2015). Neuro-fuzzy technique to predict air-overpressure induced by blasting. Arabian Journal of Geosciences,8(12), 10937–10950.

    Article  Google Scholar 

  • Army, U. (1998). Technical manual design and analysis of hardened structures to conventional weapons effects. Army TM5-855-1, Washington DC.

  • Arthur, C. K., Temeng, V. A., & Ziggah, Y. Y. (2019a). Multivariate adaptive regression splines (MARS) approach to blast-induced ground vibration prediction. International Journal of Mining, Reclamation and Environment, pp. 1–25. https://doi.org/10.1080/17480930.2019.1577940.

  • Arthur, C. K., Temeng, V. A., & Ziggah, Y. Y. (2019b). Novel approach to predicting blast-induced ground vibration using Gaussian process regression. Engineering with Computers. https://doi.org/10.1007/s00366-018-0686-3.

  • Asteris, P. G., Ashrafian, A., & Rezaie-Balf, M. (2019). Prediction of the compressive strength of self-compacting concrete using surrogate models. Computers and Concrete,24(2), 137–150.

    Google Scholar 

  • Asteris, P. G., & Plevris, V. (2017). Anisotropic masonry failure criterion using artificial neural networks. Neural Computing and Applications,28(8), 2207–2229.

    Article  Google Scholar 

  • Atashpaz-Gargari, E., & Lucas, C. (2007). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In IEEE congress on evolutionary computation. CEC 2007 (pp. 4661–4667). IEEE.

  • Ayala Carcedo, F. (2017). Drilling and blasting of rocks. Abingdon: Routledge.

    Google Scholar 

  • Ayaz, Y., Kocamaz, A. F., & Karakoç, M. B. (2015). Modeling of compressive strength and UPV of high-volume mineral-admixtured concrete using rule-based M5 rule and tree model M5P classifiers. Construction and Building Materials,94, 235–240.

    Article  Google Scholar 

  • Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees (p. 432). Belmont: International Group.

    Google Scholar 

  • Bui, X.-N., Jaroonpattanapong, P., Nguyen, H., Tran, Q.-H., & Long, N. Q. (2019a). A novel hybrid model for predicting blast-induced ground vibration based on k-nearest neighbors and particle swarm optimization. Scientific Reports,9(1), 1–14.

    Article  Google Scholar 

  • Bui, X.-N., Muazu, M. A., & Nguyen, H. (2019b). Optimizing Levenberg–Marquardt backpropagation technique in predicting factor of safety of slopes after two-dimensional OptumG2 analysis. Engineering with Computers. https://doi.org/10.1007/s00366-019-00741-0.

    Article  Google Scholar 

  • Bui, X. N., Nguyen, H., Le, H. A., Bui, H. B., & Do, N. H. (2019c). 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 

  • Ding, Z., Nguyen, H., Bui, X.-N., Zhou, J., & Moayedi, H. (2019). Computational intelligence model for estimating intensity of blast-induced ground vibration in a mine based on imperialist competitive and extreme gradient boosting algorithms. Natural Resources Research. https://doi.org/10.1007/s11053-019-09548-8.

    Article  Google Scholar 

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

    Google Scholar 

  • Frank, E., & Witten, I. H. (1998). Generating accurate rule sets without global optimization.

  • Gal, T., & Greenberg, H. J. (2012). Advances in sensitivity analysis and parametric programming (Vol. 6). Berlin: Springer.

    Google Scholar 

  • Görgülü, K., Arpaz, E., Demirci, A., Koçaslan, A., Dilmaç, M. K., & Yüksek, A. G. (2013). Investigation of blast-induced ground vibrations in the Tülü boron open pit mine. Bulletin of Engineering Geology and the Environment,72(3–4), 555–564.

    Article  Google Scholar 

  • Gou, Y., Shi, X., Huo, X., Zhou, J., Yu, Z., & Qiu, X. (2019). Motion parameter estimation and measured data correction derived from blast-induced vibration: New insights. Measurement,135, 213–230.

    Article  Google Scholar 

  • Hajihassani, M., Armaghani, D. J., Marto, A., & Mohamad, E. T. (2015a). 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 

  • Hajihassani, M., Armaghani, D. J., Monjezi, M., Mohamad, E. T., & Marto, A. (2015b). Blast-induced air and ground vibration prediction: A particle swarm optimization-based artificial neural network approach. Environmental Earth Sciences,74(4), 2799–2817.

    Article  Google Scholar 

  • Hasanipanah, M., Amnieh, H. B., Khamesi, H., Armaghani, D. J., Golzar, S. B., & Shahnazar, A. (2018). Prediction of an environmental issue of mine blasting: An imperialistic competitive algorithm-based fuzzy system. International Journal of Environmental Science and Technology,15(3), 551–560.

    Article  Google Scholar 

  • Hasanipanah, M., Faradonbeh, R. S., Amnieh, H. B., Armaghani, D. J., & Monjezi, M. (2017). Forecasting blast-induced ground vibration developing a CART model. Engineering with Computers,33(2), 307–316.

    Article  Google Scholar 

  • Hosseini, S., & Al Khaled, A. (2014). A survey on the imperialist competitive algorithm metaheuristic: Implementation in engineering domain and directions for future research. Applied Soft Computing,24, 1078–1094.

    Article  Google Scholar 

  • Khandelwal, M., Kankar, P., & Harsha, S. (2010). Evaluation and prediction of blast induced ground vibration using support vector machine. Mining Science and Technology (China),20(1), 64–70.

    Article  Google Scholar 

  • Khandelwal, M., & Singh, T. (2006). Prediction of blast induced ground vibrations and frequency in opencast mine: A neural network approach. Journal of Sound and Vibration,289(4), 711–725.

    Article  Google Scholar 

  • Khandelwal, M., & Singh, T. (2009). Prediction of blast-induced ground vibration using artificial neural network. International Journal of Rock Mechanics and Mining Sciences,46(7), 1214–1222.

    Article  Google Scholar 

  • Koopialipoor, M., Fallah, A., Armaghani, D. J., Azizi, A., & Mohamad, E. T. (2019). Three hybrid intelligent models in estimating flyrock distance resulting from blasting. Engineering with Computers,35(1), 243–256.

    Article  Google Scholar 

  • Küçüksille, E. U., Selbaş, R., & Şencan, A. (2011). Prediction of thermodynamic properties of refrigerants using data mining. Energy Conversion and Management,52(2), 836–848.

    Article  Google Scholar 

  • Marto, A., Hajihassani, M., Jahed Armaghani, D., Tonnizam Mohamad, E., & Makhtar, A. M. (2014). A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network. The Scientific World Journal, 2014, 11. https://doi.org/10.1155/2014/643715.

  • Mayor, R., & Flanders, R. (1990). Technical manual simplified computer model of air blast effects on building walls. Washington DC: US Department of State, Office of Diplomatic Security.

    Google Scholar 

  • Moayedi, H., & Armaghani, D. J. (2018). Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil. Engineering with Computers,34(2), 347–356.

    Article  Google Scholar 

  • Moayedi, H., & Hayati, S. (2018). Modelling and optimization of ultimate bearing capacity of strip footing near a slope by soft computing methods. Applied Soft Computing,66, 208–219.

    Article  Google Scholar 

  • Mohammadi, S., Kakaie, R., Ataei, M., & Pourzamani, E. (2017). Determination of the optimum cut-off grades and production scheduling in multi-product open pit mines using imperialist competitive algorithm (ICA). Resources Policy,51, 39–48.

    Article  Google Scholar 

  • Mohammadnejad, M., Gholami, R., Ramezanzadeh, A., & Jalali, M. (2012). Prediction of blast-induced vibrations in limestone quarries using support vector machine. Journal of Vibration and Control,18(9), 1322–1329.

    Article  Google Scholar 

  • Monjezi, M., Ahmadi, M., Sheikhan, M., Bahrami, A., & Salimi, A. (2010). Predicting blast-induced ground vibration using various types of neural networks. Soil Dynamics and Earthquake Engineering,30(11), 1233–1236.

    Article  Google Scholar 

  • Monjezi, M., Ghafurikalajahi, M., & Bahrami, A. (2011). Prediction of blast-induced ground vibration using artificial neural networks. Tunnelling and Underground Space Technology,26(1), 46–50.

    Article  Google Scholar 

  • Monjezi, M., Hasanipanah, M., & Khandelwal, M. (2013). Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network. Neural Computing and Applications,22(7–8), 1637–1643.

    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. https://doi.org/10.1007/s42452-019-0295-9.

    Article  Google Scholar 

  • Nguyen, H., & Bui, X.-N. (2019). Predicting blast-induced air overpressure: A robust artificial intelligence system based on artificial neural networks and random forest. Natural Resources Research,28, 893–907.

    Article  Google Scholar 

  • 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.

    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., & 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.

    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. (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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • Quinlan, J. R. (1987). Simplifying decision trees. International Journal of Man-Machine Studies,27(3), 221–234.

    Article  Google Scholar 

  • Quinlan, J. R. (1992). Learning with continuous classes. In: 5th Australian joint conference on artificial intelligence (Vol. 92, pp. 343–348). Singapore: World Scientific.

  • Rakovec, O., Hill, M. C., Clark, M., Weerts, A., Teuling, A., & Uijlenhoet, R. (2014). Distributed evaluation of local sensitivity analysis (DELSA), with application to hydrologic models. Water Resources Research,50(1), 409–426.

    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 

  • Shafiullah, G., Simson, S., Thompson, A., Wolfs, P. J., & Ali, A. (2008). Forecasting vertical acceleration railway wagons—A comparative study. Paper presented at the 4th international conference on data mining (DMIN’08), Las Vegas, NV.

  • 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.

    Article  Google Scholar 

  • Shi, X.-Z., Jian, Z., 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.

    Article  Google Scholar 

  • Sikora, M., Krzystanek, Z., Bojko, B., & Śpiechowicz, K. (2011). Application of a hybrid method of machine learning for description and on-line estimation of methane hazard in mine workings. Journal of Mining Science,47(4), 493–505.

    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 

  • Talatahari, S., Azar, B. F., Sheikholeslami, R., & Gandomi, A. (2012). Imperialist competitive algorithm combined with chaos for global optimization. Communications in Nonlinear Science and Numerical Simulation,17(3), 1312–1319.

    Article  Google Scholar 

  • Wu, Y., Hao, H., Zhou, Y., & Chong, K. (1998). Propagation characteristics of blast-induced shock waves in a jointed rock mass. Soil Dynamics and Earthquake Engineering,17(6), 407–412.

    Article  Google Scholar 

  • Zhang, X., Nguyen, H., Bui, X.-N., Tran, Q.-H., Nguyen, D.-A., Bui, D. T., & Moayedi, H. (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.

  • Zhou, J., Li, X., & Mitri, H. S. (2016a). 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, E., Yang, S., Wang, M., Shi, X., Yao, S., et al. (2019). 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.

    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., Shi, X., Du, K., Qiu, X., Li, X., & Mitri, H. S. (2016b). Feasibility of random-forest approach for prediction of ground settlements induced by the construction of a shield-driven tunnel. International Journal of Geomechanics,17(6), 04016129.

    Article  Google Scholar 

Download references

Acknowledgments

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. Institute of Architecture Engineering, Huanghuai University, Zhumadian, 463000, Henan, China

    Qiancheng Fang

  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, 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, Duc Thang Ward, Bac Tu Liem District, Hanoi, Vietnam

    Xuan-Nam Bui

  5. Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Trung Nguyen-Thoi

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

    Trung Nguyen-Thoi

Authors
  1. Qiancheng Fang
    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. Trung Nguyen-Thoi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hoang Nguyen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fang, Q., Nguyen, H., Bui, XN. et al. Prediction of Blast-Induced Ground Vibration in Open-Pit Mines Using a New Technique Based on Imperialist Competitive Algorithm and M5Rules. Nat Resour Res 29, 791–806 (2020). https://doi.org/10.1007/s11053-019-09577-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11053-019-09577-3

Keywords

Search

Navigation