Skip to main content
Log in

Evaluation and Assessment of Blast-Induced Ground Vibrations in an Underground Gold Mine: A Case Study

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

Abstract

Ground vibrations induced during rock fragmentation by blasting remain a potential source of hazard for the stability of nearby structures. In this paper, to forecast the effect of blast-induced ground vibrations, dimensional analysis (DA) is proposed to predict peak particle velocity (PPV). In conventional predictor equations, the major and critical parameter for the estimation of PPV is square root scaled distance. The new formula based on DA was obtained considering various blast design parameters in order to improve the capability of PPV prediction. After obtaining the new DA equation for the prediction of PPV, 360 data sets were used to determine the unknown coefficients of the new equation as well as site constants of different conventional predictor equations. Then, ten additional randomly selected data sets were used to compare the capability of the new model with conventional predictor equations. The results were compared based on coefficient of determination (R2) and mean absolute error (MAE) between measured and predicted values of PPV. The proposed formula with the greatest R2 and the lowest MAE was the better option for predicting the PPV of induced vibrations for the measured field data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11

Similar content being viewed by others

References

  • Afeni, T. B., & Osasan, S. K. (2009). Assessment of noise and ground vibration induced during blasting operations in an open pit mine—A case study on Ewekoro limestone quarry, Nigeria. Mining Science and Technology (chinA), 19(4), 420–424.

    Google Scholar 

  • Alhama, F., & Madrid, C. N. (2007). Discriminated dimensional analysis versus classical dimensional analysis, Applications to heat transfer and fluid dynamics. CJChE, 15(5), 626–631.

    Google Scholar 

  • Alvarez-Vigil, A. E., Gonzales-Nicieza, C., Lopez Gayarre, F., & Alvarez-Fernandez, M. I. (2012). Predicting blasting propagation velocity and vibration frequency using artificial neural network. International Journal of Rock Mechanics and Mining Sciences, 55, 108–116.

    Google Scholar 

  • Armaghani, D. J., Momeni, E., Abad, S. V. A. N. K., et al. (2015). Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting. Environment and Earth Science, 74, 2845–2860.

    Google Scholar 

  • Bakhshandeh Amnieh, H., 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.

    Google Scholar 

  • Bakhtavar, E., Abdollahisharif, J., & Ahmadi, M. (2017b). Reduction of the undesirable bench-blasting consequences with emphasis on ground vibration using a developed multi-objective stochastic programming. International Journal of Mining, Reclamation and Environment, 31(5), 333–345.

    Google Scholar 

  • Bakhtavar, E., Khoshrou, H., & Badroddin, M. (2015). Using dimensional-regression analysis to predict the mean particle size of fragmentation by blasting at the Sungun copper mine. Arabian Journal of Geosciences, 8, 2111–2120.

    Google Scholar 

  • Bakhtavar, E., Nourizadeh, H., & Sahebi, A. A. (2017a). Toward predicting blast-induced flyrock: A hybrid dimensional analysis fuzzy inference system. International Journal of Environmental Science and Technology, 14, 717–728.

    Google Scholar 

  • Bakhtavar, E., & Yousefi, S. (2019). Analysis of ground vibration risk on mine infrastructures: Integrating fuzzy slack-based measure model and failure effects analysis. International Journal of Environmental Science and Technology, 16, 6065–6076.

    Google Scholar 

  • Bayat, P., Monjezi, M., Mehrdanesh, A., et al. (2021). Blasting pattern optimization using gene expression programming and grasshopper optimization algorithm to minimise blast-induced ground vibrations. Engineering with Computers. https://doi.org/10.1007/s00366-021-01336-4

    Article  Google Scholar 

  • Bui, X. N., Nguyen, H., Tran, Q. H., et al. (2021). Predicting ground vibrations due to mine blasting using a novel artificial neural network-based cuckoo search optimization. Natural Resources Research, 30, 2663–2685.

    Google Scholar 

  • Bureau of Indian Standard. (1973). Criteria for safety and design of structures subjected to underground blast.

  • Chen, S., Zhang, Z., & Wu, J. (2015). Human comfort evaluation criteria for blast planning. Environmental Earth Sciences, 74(4), 2919–2923.

    Google Scholar 

  • Cheng, C. L., & Garg, G. (2014). Coefficient of determination for multiple measurement error models. Journal of Multivariate Analysis, 126, 137–152.

    Google Scholar 

  • Cheng, K. M., & Chau, K. T. (2015). Attenuation of blasting induced peak particle velocity: Constructing a new empirical formula. Crc Press-Taylor & Francis Group.

    Google Scholar 

  • Dehghani, H., & Ataee-pour, M. (2011). Development of a model to predict peak particle velocity in a blasting operation. International Journal of Rock Mechanics and Mining Sciences, 48(1), 51–58.

    Google Scholar 

  • Ding, Z., Nguyen, H., Bui, X. N., et al. (2020). 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, 29, 751–769.

    Google Scholar 

  • Duvall, W. I., Fogelson, D. E., & USBM. (1962). Review of criteria for estimating damage to residences from blasting vibrations. Retrieved from http://www.osmre.gov/resources/blasting/docs/USBM/RI5968EstimatingDamagesResidences.pdf.

  • Fang, Q., Nguyen, H., Bui, X. N., et al. (2020). Prediction of blast-induced ground vibration in open-pit mines using a new technique based on imperialist competitive algorithm and M5Rules. Natural ResourCes Research, 29, 791–806.

    Google Scholar 

  • Fattahi, H., & Hasanipanah, M. (2021). Prediction of blast-induced ground vibration in a mine using relevance vector regression optimized by metaheuristic algorithms. Natural Resources Research, 30, 1849–1863.

    Google Scholar 

  • Fisne, A., Kuzu, C., & Hudaverdi, T. (2011). Prediction of environmental impacts of quarry blasting operation using fuzzy logic. Environmental Monitoring and Assessment, 174(1–4), 461–470.

    Google Scholar 

  • He, Z., Armaghani, D. J., Masoumnezhad, M., et al. (2021). A combination of expert-based system and advanced decision-tree algorithms to predict air-overpressure resulting from quarry blasting. Natural Resources Research, 30, 1889–1903. https://doi.org/10.1007/s11053-020-09773-6

    Article  Google Scholar 

  • Hendron, A., & Ambraseys, N. (1968). Dynamic behaviour of rock masses (Zienkiewicz OC ed). Wiley.

    Google Scholar 

  • Khandelwal, M. (2010). Evaluation and prediction of blast induced ground vibration using support vector machine. International Journal of Rock Mechanics & Mining Sciences, 47(3), 509–516.

    Google Scholar 

  • Khandelwal, M. (2011). Blast-induced ground vibration prediction using support vector machine. Engineering with Computers, 27(3), 193–200.

    Google Scholar 

  • Khandelwal, M. (2012). Application of an expert system for the assessment of blast vibration. Geotechnical and Geological Engineering, 30(1), 205–217.

    Google Scholar 

  • Khandelwal, M., Armaghani, D. J., Faradonbeh, R. S., et al. (2017). Classification and regression tree technique in estimating peak particle velocity caused by blasting. Engineering with Computers, 33(1), 45–53.

    Google Scholar 

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

    Google Scholar 

  • Khandelwal, M., Kumar, D. L., & Yellishetty, M. (2011). Application of soft computing to predict blast-induced ground vibration. Engineering with Computers, 27(2), 117–125.

    Google Scholar 

  • Khandelwal, M., & Saadat, M. (2015). A dimensional analysis approach to study blast-induced ground vibration. Rock Mechanics and Rock Engineering, 48(2), 727–735.

    Google Scholar 

  • Khandelwal, M., & Singh, T. N. (2006). Prediction of blast induced ground vibrations and frequency in opencast mine: A neural network approach. Journal of Sound and Vibration, 289(4–5), 711–725.

    Google Scholar 

  • Khandelwal, M., & Singh, T. N. (2007). Evaluation of blast-induced ground vibration predictors. Soil Dynamics and Earthquake Engineering, 27(2), 116–125.

    Google Scholar 

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

    Google Scholar 

  • Khandelwal, M., & Singh, T. N. (2013). Application of an expert system to predict maximum explosive charge used per delay in surface mining. Rock Mechanics and Rock Engineering, 46(6), 1551–1558.

    Google Scholar 

  • Langefors, U., & Kihlström, B. (1963). The modern technique of rock blasting (Vol. 1). Wiley.

    Google Scholar 

  • Mohamadnejad, M., Gholami, R., & Ataei, M. (2012). Comparison of intelligence science techniques and empirical methods for prediction of blasting vibrations. Tunnelling and Underground Space Technology, 28, 238–244.

    Google Scholar 

  • Mohammad, M. T. (2009). Artificial neural network for prediction and control of blasting vibration in Assiut (Egypt) limestone quarry. International Journal of Rock Mechanics and Mining Sciences, 46(2), 426–431.

    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.

    Google Scholar 

  • Monjezi, M., Singh, T. N., Khandelwal, M., Sinha, S., Singh, V., & Hosseini, I. (2006). Prediction and analysis of blast parameters using artificial neural network. Noise & Vibration Worldwide, 37(5), 8–16.

    Google Scholar 

  • Qiu, Y., Zhou, J., Khandelwal, M., Yang, H., Yang, P., & Li, C. (2021). Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration. Engineering with Computers. https://doi.org/10.1007/s00366-021-01393-9

    Article  Google Scholar 

  • Reddy, G. M., & Reddy, V. D. (2014). Theoretical investigations on dimensional analysis of ball bearing parameters by using buckingham pi-theorem. Procedia Engineering, 97, 1305–1311.

    Google Scholar 

  • Rezaeineshat, A., Monjezi, M., Mehrdanesh, A., et al. (2020). Optimization of blasting design in open pit limestone mines with the aim of reducing ground vibration using robust techniques. Geomechanics and Geophysics for Geo-Energy and Geo-Resources, 6, 1–14.

    Google Scholar 

  • Saadat, M., Hasanzade, A., & Khandelwal, M. (2015). Differential evolution algorithm for predicting blast induced ground vibrations. International Journal of Rock Mechanics and Mining Sciences, 77, 97–104.

    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.

    Google Scholar 

  • Sanchidrián, J. A., & Ouchterlony, F. A. (2017). Distribution-free description of fragmentation by blasting based on dimensional analysis. Rock Mechanics and Rock Engineering, 50, 781–806.

    Google Scholar 

  • Sharp, J. J., Deb, A., & Deb, M. K. (1992). Applications of matrix manipulation in dimensional analysis involving large numbers of variables. Marine Structures, 5(4), 333–348.

    Google Scholar 

  • Simangunsong, G. M., & Wahyudi, S. (2015). Effect of bedding plane on prediction blast-induced ground vibration in open pit coal mines. International Journal of Rock Mechanics and Mining Sciences, 79, 1–8.

    Google Scholar 

  • Zhang, X., Nguyen, H., Bui, X. N., Tran, Q. H., Nguyen, D. A., Bui, D. T., & Moayedi, H. (2020). Novel soft computing model for predicting blast-induced ground vibration in open-pit mines based on particle swarm optimization and XGBoost. Natural Resources Research, 29(2), 711–721.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Manoj Khandelwal.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tribe, J., Koroznikova, L., Khandelwal, M. et al. Evaluation and Assessment of Blast-Induced Ground Vibrations in an Underground Gold Mine: A Case Study. Nat Resour Res 30, 4673–4694 (2021). https://doi.org/10.1007/s11053-021-09943-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11053-021-09943-0

Keywords

Navigation