Skip to main content
SpringerLink
Log in

Optimization of blasting design in open pit limestone mines with the aim of reducing ground vibration using robust techniques

  • Original Article
  • Published:
Geomechanics and Geophysics for Geo-Energy and Geo-Resources Aims and scope Submit manuscript

Abstract

Blasting operations create significant problems to residential and other structures located in the close proximity of the mines. Blast vibration is one of the most crucial nuisances of blasting, which should be accurately estimated to minimize its effect. In this paper, an attempt has been made to apply various models to predict ground vibrations due to mine blasting. To fulfill this aim, 112 blast operations were precisely measured and collected in one the limestone mines of Iran. These blast operation data were utilized to construct the artificial neural network (ANN) model to predict the peak particle velocity (PPV). The input parameters used in this study were burden, spacing, maximum charge per delay, distance from blast face to monitoring point and rock quality designation and output parameter was the PPV. The conventional empirical predictors and multivariate regression analysis were also performed on the same data sets to study the PPV. Accordingly, it was observed that the ANN model is more accurate as compared to the other employed predictors. Moreover, it was also revealed that the most influential parameters on the ground vibration are distance from the blast and maximum charge per delay, whereas the least effective parameters are burden, spacing and rock quality designation. Finally, in order to minimize PPV, the developed ANN model was used as an objective function for imperialist competitive algorithm (ICA). Eventually, it was found that the ICA algorithm is able to decrease PPV up to 59% by considering burden of 2.9 m, spacing of 4.4 m and charge per delay of 627 Kg.

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
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  • Ambraseys N (1968) Rock mechanics in engineering practice. Wiley, Hoboken

    Google Scholar 

  • Armaghani DJ, Hajihassani M, Mohamad ET, Marto A, Noorani S (2014) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab J Geosci 7(12):5383–5396

    Google Scholar 

  • Armaghani DJ, Momeni E, Abad SVANK, Khandelwal M (2015) Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting. Environ Earth Sci 74(4):2845–2860

    Google Scholar 

  • Armaghani DJ, Faradonbeh RS, Rezaei H, Rashid ASA, Amnieh HB (2018a) Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming. Neural Comput Appl 29(11):1115–1125

    Google Scholar 

  • Armaghani DJ, Hasanipanah M, Amnieh HB, Mohamad ET (2018b) Feasibility of ICA in approximating ground vibration resulting from mine blasting. Neural Comput Appl 29(9):457–465

    Google Scholar 

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

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

  • Chen G, Fu K, Liang Z, Sema T, Li C, Tontiwachwuthikul P, Idem R (2014) The genetic algorithm based back propagation neural network for MMP prediction in CO 2-EOR process. Fuel 126:202–212

    Google Scholar 

  • Dindarloo SR (2015) Prediction of blast-induced ground vibrations via genetic programming. Int J Min Sci Technol 25(6):1011–1015

    Google Scholar 

  • Dowding CH, Dowding C (1996) Construction vibrations. Prentice Hall, Upper Saddle River

    Google Scholar 

  • Dowding CH, Hryciw RD (1986) A laboratory study of blast densification of saturated sand. J Geotech Eng 112(2):187–199

    Google Scholar 

  • Duvall WI, Fogelson DE (1962) Review of criteria for estimating damage to residences from blasting vibrations. US Department of the Interior, Bureau of Mines

  • Duvall WI, Petkof B (1958) Spherical propagation of explosion-generated strain pulses in rock. Bureau of Mines, Bangalore

    Google Scholar 

  • Dyskin AV, Basarir H, Doherty J et al (2018) Computational monitoring in real time: review of methods and applications. Geomech Geophys Geo-energ Geo-resour 4:235. https://doi.org/10.1007/s40948-018-0086-6

    Article  Google Scholar 

  • Faradonbeh RS, Monjezi M (2017) Prediction and minimization of blast-induced ground vibration using two robust meta-heuristic algorithms. Eng Comput 33(4):835–851

    Google Scholar 

  • Fişne A, Kuzu C, Hüdaverdi T (2011) Prediction of environmental impacts of quarry blasting operation using fuzzy logic. Environ Monit Assess 174(1–4):461–470

    Google Scholar 

  • Ghasemi E, Ataei M, Hashemolhosseini H (2013) Development of a fuzzy model for predicting ground vibration caused by rock blasting in surface mining. J Vib Control 19(5):755–770

    Google Scholar 

  • Gokceoglu C, Zorlu K (2004) A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock. Eng Appl Artif Intell 17(1):61–72

    Google Scholar 

  • Grima MA, Verhoef P (1999) Forecasting rock trencher performance using fuzzy logic. Int J Rock Mech Min Sci 36(4):413–432

    Google Scholar 

  • Hajihassani M, Armaghani DJ, Marto A, Mohamad ET (2015a) Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bull Eng Geol Env 74(3):873–886

    Google Scholar 

  • Hajihassani M, Armaghani DJ, Monjezi M, Mohamad ET, Marto A (2015b) Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach. Environ Earth Sci 74(4):2799–2817

    Google Scholar 

  • Hasanipanah M, Monjezi M, Shahnazar A, Armaghani DJ, Farazmand A (2015) Feasibility of indirect determination of blast induced ground vibration based on support vector machine. Measurement 75:289–297

    Google Scholar 

  • Hasanipanah M, Faradonbeh RS, Amnieh HB, Armaghani DJ, Monjezi M (2017) Forecasting blast-induced ground vibration developing a CART model. Eng Comput 33(2):307–316

    Google Scholar 

  • Hosseini S, Khaled AA (2014) A survey on the Imperialist Competitive Algorithm metaheuristic: implementation in engineering domain and directions for future research. Appl Soft Comput 24:1078–1094

    Google Scholar 

  • Hudaverdi T (2012) Application of multivariate analysis for prediction of blast-induced ground vibrations. Soil Dyn Earthq Eng 43:300–308

    Google Scholar 

  • Iphar M, Yavuz M, Ak H (2008) Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system. Environ Geol 56(1):97–107

    Google Scholar 

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

    Google Scholar 

  • Khandelwal M, Singh T (2007) Evaluation of blast-induced ground vibration predictors. Soil Dyn Earthq Eng 27(2):116–125

    Google Scholar 

  • Khandelwal M, Singh T (2009) Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46(7):1214–1222

    Google Scholar 

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

    Google Scholar 

  • Khandelwal M, Kumar DL, Yellishetty M (2011) Application of soft computing to predict blast-induced ground vibration. Eng Comput 27(2):117–125

    Google Scholar 

  • Khandelwal M, Ranjith PG (2017) Study of crack propagation in concrete under multiple loading rates by acoustic emission. Geomech Geophys Geo-energ Geo-resour 3:393–404. https://doi.org/10.1007/s40948-017-0067-1

    Article  Google Scholar 

  • 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(1):45–53

    Google Scholar 

  • Kosko B (1992) Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence/book and disk. Prentice Hall, Upper Saddle River

    MATH  Google Scholar 

  • Li DT, Yan JL, Zhang L (2012) Prediction of Blast-Induced Ground Vibration Using Support Vector Machine by Tunnel Excavation. Appl Mech Mater 170–173:1414–1418. https://doi.org/10.4028/www.scientific.net/amm.170-173.1414

    Article  Google Scholar 

  • Mehrdanesh A, Monjezi M, Sayadi AR (2018) Evaluation of effect of rock mass properties on fragmentation using robust techniques. Eng Comput 34(2):253–260

    Google Scholar 

  • Mohamadnejad M, Gholami R, Ataei M (2012) Comparison of intelligence science techniques and empirical methods for prediction of blasting vibrations. Tunn Undergr Space Technol 28:238–244

    Google Scholar 

  • Mohamed MT (2011) Performance of fuzzy logic and artificial neural network in prediction of ground and air vibrations. Int J Rock Mech Min Sci 48(5):845

    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 Dyn Earthq Eng 30(11):1233–1236

    Google Scholar 

  • Monjezi M, Ghafurikalajahi M, Bahrami A (2011) Prediction of blast-induced ground vibration using artificial neural networks. Tunn Undergr Space Technol 26(1):46–50

    Google Scholar 

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

    Google Scholar 

  • Monjezi M, Mehrdanesh A, Malek A, Khandelwal M (2013b) Evaluation of effect of blast design parameters on flyrock using artificial neural networks. Neural Comput Appl 23(2):349–356

    Google Scholar 

  • Monjezi M, Baghestani M, Faradonbeh RS, Saghand MP, Armaghani DJ (2016) Modification and prediction of blast-induced ground vibrations based on both empirical and computational techniques. Eng Comput 32(4):717–728

    Google Scholar 

  • Nateghi R (2011) Prediction of ground vibration level induced by blasting at different rock units. Int J Rock Mech Min Sci 48(6):899–908

    Google Scholar 

  • Neaupane KM, Achet SH (2004) Use of backpropagation neural network for landslide monitoring: a case study in the higher Himalaya. Eng Geol 74(3):213–226

    Google Scholar 

  • Rahul KM, Rai R, Shrivastva BK (2015) Evaluation of dump slope stability of a coal mine using artificial neural network. Geomech Geophys Geo-energ Geo-resour 1:69–77

    Google Scholar 

  • Simpson PK (1990) Artificial neural systems: foundations, paradigms, applications, and implementations. Pergamon, Oxford

    Google Scholar 

  • Singh T, Singh V (2005) An intelligent approach to prediction and control ground vibration in mines. Geotech Geol Eng 23(3):249–262

    Google Scholar 

  • Trippi RR, Turban E (1992) Neural networks in finance and investing: using artificial intelligence to improve real world performance. McGraw-Hill Inc, New York

    Google Scholar 

  • Verma A, Singh T (2011) Intelligent systems for ground vibration measurement: a comparative study. Eng Comput 27(3):225–233

    Google Scholar 

  • Verma AK, Sirvaiya A (2016) Comparative analysis of intelligent models for prediction of Langmuir constants for CO2 adsorption of Gondwana coals in India. Geomech Geophys Geo-energ Geo-resour 2:97–109. https://doi.org/10.1007/s40948-016-0025-3

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering, Tarbiat Modares University, Tehran, Iran

    Afsaneh Rezaeineshat, Masoud Monjezi & Amirhossein Mehrdanesh

  2. School of Science, Engineering and Information Technology, Federation University Australia, PO Box 663, Ballarat, Victoria, 3353, Australia

    Manoj Khandelwal

Authors
  1. Afsaneh Rezaeineshat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Masoud Monjezi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amirhossein Mehrdanesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Manoj Khandelwal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Masoud Monjezi.

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

Rezaeineshat, A., Monjezi, M., Mehrdanesh, A. et al. Optimization of blasting design in open pit limestone mines with the aim of reducing ground vibration using robust techniques. Geomech. Geophys. Geo-energ. Geo-resour. 6, 40 (2020). https://doi.org/10.1007/s40948-020-00164-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40948-020-00164-y

Keywords

Search

Navigation