Skip to main content
SpringerLink
Log in

Predicting Blast-Induced Ground Vibrations in Some Indian Tunnels: a Comparison of Decision Tree, Artificial Neural Network and Multivariate Regression Methods

  • Published:
Mining, Metallurgy & Exploration Aims and scope Submit manuscript

Abstract

The present study compares three different techniques (decision tree, artificial neural network and multivariate regression analysis) for predicting blast-induced ground vibrations in some Indian tunnelling projects. The performance of these models was also compared to site-specific conventional predictor equations. A database consisting of 137 vibration records was randomly divided into training and testing sets for model generation. Eight input parameters (total charge, tunnel cross-section, maximum charge per delay, number of holes, hole diameter, distance from blasting face, hole depth and charge per hole) were selected for model development using bivariate correlation analysis. Results indicated that the decision tree is best suited for predicting vibrations. The decision tree further suggested that the intensity of near-field ground vibrations is mainly affected by total charge fired in a round, whereas the intensity of far-field vibrations is governed by maximum charge per delay and charge per hole. Conventional ground vibration predictors and machine learning techniques such as neural networks do not depict the relationship between input and output parameters. However, the present study substantiates that the decision tree can be a good tool for precise prediction of ground vibrations. Further, the decision tree can classify and relate different blast design parameters for refining blast designs to control ground vibrations on sites.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Abbreviations

ANN:

Artificial neural network

CART:

Classification and regression tree

MCPD:

Maximum charge per delay

MVRA:

Multivariate regression analysis

PPV:

Peak particle velocity

RMSE:

Root mean square error

References

  1. Satici O, Hindistan A (2000) Drilling and blasting as a tunnel excavation method. Dissertation, Middle East Technical University.

  2. Monjezi M, Dehghani H (2008) Evaluation of effect of blasting pattern parameters on back break using neural networks. Int J Rock Mech Min Sci 45:1446–1453 https://doi.org/10.1016/j.ijrmms.2008.02.007

    Article  Google Scholar 

  3. Ak H, Konuk A (2008) The effect of discontinuity frequency on ground vibrations produced from bench blasting: a case study. Soil Dyn Earthq Eng 28:686–694 https://doi.org/10.1016/j.soildyn.2007.11.006

    Article  Google Scholar 

  4. Khandelwal M, Singh TN (2009) Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46:1214–1222 https://doi.org/10.1016/j.ijrmms.2009.03.004

    Article  Google Scholar 

  5. Hasanipanah M, Faradonbeh RS, Amnieh HB, Armaghani DJ, Monjezi M (2017) Forecasting blast-induced ground vibration developing a CART model. Eng Comput 33:307–316 https://doi.org/10.1007/s00366-016-0475-9

    Article  Google Scholar 

  6. Khandelwal M, Armaghani DJ, Faradonbeh RS, Yellishetty M, Majid MZA, Monjezi M (2017) Classification and regression tree technique in estimating peak particle velocity caused by blasting. Eng Comput 33:45–53 https://doi.org/10.1007/s00366-016-0455-0

    Article  Google Scholar 

  7. Khandelwal M, Singh TN (2006) Prediction of blast induced ground vibrations and frequency in opencast mine: a neural network approach. J Sound Vib 289:711–725 https://doi.org/10.1016/j.jsv.2005.02.044

    Article  Google Scholar 

  8. Meulenkamp F, Alvarez Grima M (1999) Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness. Int J Rock Mech Min Sci 36:29–39 https://doi.org/10.1016/S0148-9062(98)00173-9

    Article  Google Scholar 

  9. Rukhaiyar S, Samadhiya NK (2017) A polyaxial strength model for intact sandstone based on artificial neural network. Int J Rock Mech Min Sci 95:26–47 https://doi.org/10.1016/j.ijrmms.2017.03.012

    Article  Google Scholar 

  10. Bakhshandeh Amnieh H, Siamaki A, Soltani S (2012) Design of blasting pattern in proportion to the peak particle velocity (PPV): artificial neural networks approach. Saf Sci 50:1913–1916 https://doi.org/10.1016/j.ssci.2012.05.008

    Article  Google Scholar 

  11. Monjezi M, Mehrdanesh A, Malek A, Khandelwal M (2013) Evaluation of effect of blast design parameters on flyrock using artificial neural networks. Neural Comput Applic 23:349–356 https://doi.org/10.1007/s00521-012-0917-2

    Article  Google Scholar 

  12. Mohamed MT (2009) Artificial neural network for prediction and control of blasting vibrations in Assiut (Egypt) limestone quarry. Int J Rock Mech Min Sci 46:426–431 https://doi.org/10.1016/j.ijrmms.2008.06.004

    Article  Google Scholar 

  13. Alvarez-Vigil AE, Gonzalez-Nicieza C, Lopez Gayarre F, Alvarez-Fernandez MI (2012) Predicting blasting propagation velocity and vibration frequency using artificial neural networks. Int J Rock Mech Min Sci 55:108–116 https://doi.org/10.1016/j.ijrmms.2012.05.002

    Article  Google Scholar 

  14. Zhongya Z, Xiaoguang J (2018) Prediction of peak velocity of blasting vibration based on artificial neural network optimized by dimensionality reduction of FA-MIV. Math Probl Eng. https://doi.org/10.1155/2018/8473547

  15. Mohamadnejad M, Gholami R, Ataei M (2012) Comparison of intelligence science techniques and empirical methods for prediction of blasting vibrations. Tunn Undergr Sp Technol 28:238–244. https://doi.org/10.1016/j.tust.2011.12.001

    Article  Google Scholar 

  16. 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. J Rock Mech Geotech Eng 6:67–76. https://doi.org/10.1016/j.jrmge.2013.11.001

    Article  Google Scholar 

  17. Lapcevic R, Kostic S, Pantovic R, Vasovic N (2014) Prediction of blast-induced ground motion in a copper mine. Int J Rock Mech Min Sci 69:19–25. https://doi.org/10.1016/j.ijrmms.2014.03.002

    Article  Google Scholar 

  18. Atkinson PM, Tatnall ARL (1997) Introduction: neural networks in remote sensing. Int J Remote Sens 18:699–709. https://doi.org/10.1080/014311697218700

    Article  Google Scholar 

  19. Kanungo DP, Arora MK, Sarkar S, Gupta RP (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Eng Geol 85:347–366. https://doi.org/10.1016/j.enggeo.2006.03.004

    Article  Google Scholar 

  20. Rafiai H, Jafari A, Mahmoudi A (2013) Application of ANN-based failure criteria to rocks under polyaxial stress conditions. Int J Rock Mech Min Sci 59:42–49. https://doi.org/10.1016/j.ijrmms.2012.12.003

    Article  Google Scholar 

  21. Maji VB, Sitharam TG (2008) Prediction of elastic modulus of jointed rock mass using artificial neural networks. Geotech Geol Eng 26:443–452. https://doi.org/10.1007/s10706-008-9180-9

    Article  Google Scholar 

  22. Ocak I, Seker SE (2012) Estimation of elastic modulus of intact rocks by artificial neural network. Rock Mech Rock Eng 45:1047–1054. https://doi.org/10.1007/s00603-012-0236-z

    Article  Google Scholar 

  23. Rafiai H, Jafari A (2011) Artificial neural networks as a basis for new generation of rock failure criteria. Int J Rock Mech Min Sci 48:1153–1159. https://doi.org/10.1016/j.ijrmms.2011.06.001

    Article  Google Scholar 

  24. Rukhaiyar S, Samadhiya NK (2017) A polyaxial strength model for intact sandstone based on artificial neural network. Int J Rock Mech Min Sci 95:26–47. https://doi.org/10.1016/j.ijrmms.2017.03.012

    Article  Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge the generous time and insightful comments provided by anonymous peer reviewers to enhance the quality of the paper.

Funding

This work was supported by the Council of Scientific and Industrial Research-Central Institute of Mining and Fuel Research, India, by grant number MLP-105/18-19.

Author information

Authors and Affiliations

  1. CSIR-Central Institute of Mining and Fuel Research, Dhanbad, Jharkhand, India

    Aditya Rana, N. K. Bhagat, G. P. Jadaun, Saurav Rukhaiyar & P. K. Singh

  2. CSIR-Central Building Research Institute, Roorkee, Uttarakhand, India

    Anindya Pain

Authors
  1. Aditya Rana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. K. Bhagat
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. G. P. Jadaun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Saurav Rukhaiyar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Anindya Pain
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. P. K. Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aditya Rana.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rana, A., Bhagat, N.K., Jadaun, G.P. et al. Predicting Blast-Induced Ground Vibrations in Some Indian Tunnels: a Comparison of Decision Tree, Artificial Neural Network and Multivariate Regression Methods. Mining, Metallurgy & Exploration 37, 1039–1053 (2020). https://doi.org/10.1007/s42461-020-00205-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42461-020-00205-w

Keywords

Search

Navigation