Skip to main content
SpringerLink
Log in

Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

Mines, quarries, and construction sites face environmental damages due to blasting environmental impacts such as ground vibration and air overpressure. These phenomena may cause damage to structures, groundwater, and ecology of the nearby area. Several empirical predictors have been proposed by various scholars to estimate ground vibration and air overpressure, but these methods are inapplicable in many conditions. However, prediction of ground vibration and air overpressure is complicated as a consequence of the fact that a large number of influential parameters are involved. In this study, a hybrid model of an artificial neural network and a particle swarm optimization algorithm was implemented to predict ground vibration and air overpressure induced by blasting. To develop this model, 88 datasets including the parameters with the greatest influence on ground vibration and air overpressure were collected from a granite quarry site in Malaysia. The results obtained by the proposed model were compared with the measured values as well as with the results of empirical predictors. The results indicate that the proposed model is an applicable and accurate tool to predict ground vibration and air overpressure induced by blasting.

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

Similar content being viewed by others

References

  • Adhikari R, Agrawal RK (2011) Effectiveness of PSO based neural network for seasonal time series forecasting. In: Proceedings of the Indian international conference on artificial intelligence (IICAI), Tumkur, India, pp 232–244

  • Baker WE, Cox PA, Kulesz JJ, Strehlow RA, Westine PS (1983) Explosion hazards and evaluation. Elsevier Science, Amsterdam

    Google Scholar 

  • Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 43:3–31

    Article  Google Scholar 

  • Basu D, Sen M (2005) Blast induced ground vibration norms—a critical review. National seminar on policies statutes and legislation in Mines Kharagpur, India, pp 112–113

  • Bhandari S (1997) Engineering rock blasting operations. AA Balkema, Rotterdam

    Google Scholar 

  • Bounds DG, Lloyd PJ, Mathew B, Waddell G (1998) A multilayer perceptron network for the diagnosis of low back pain. In: IEEE international conference on neural networks, pp 481–489

  • Bureau of Indian Standard (1973) Criteria for safety and design of structures subjected to underground blast. ISI Bull, IS-6922

  • Cengiz K (2008) The importance of site-specific characters in prediction models for blast-induced ground vibrations. Soil Dyn Earthq Eng 28:405–414

    Article  Google Scholar 

  • Clerc M, Kennedy J (2002) The particle swarm explosion, stability, and convergence in a multi-dimensional complex space. IEEE Trans Evolut Comput 6:58–73

    Article  Google Scholar 

  • Davies B, Farmer IW, Attewell PB (1964) Ground vibrations from shallow sub-surface blasts. Eng Lond 217:553–559

    Google Scholar 

  • Diamantidis NA, Karlis D, Giakoumakis EA (2000) Unsupervised stratification of cross-validation for accuracy estimation. Artif Intell 116:1–16

    Article  Google Scholar 

  • Douglas E (1989) An investigation of blasting criteria for structural and ground vibrations. Master Thesis, Ohio University

  • Dowding CH (1985) Blast vibration monitoring and control. Prentice-Hall, Englewoods Cliffs, pp 288–290

    Google Scholar 

  • Dowding CH (ed) (2000) Construction vibrations. pp 204–207

  • Dreyfus G (2005) Neural Networks: methodology and application. Springer, Berlin, Heidelberg

    Google Scholar 

  • Duvall WI, Petkof B (1959) Spherical propagation of explosion of generated strain pulses in rocks. USBM, RI-5483

  • Duvall WI, Johnson CF, Meyer AVC (1963) Vibrations from blasting at Iowa limestone quarries. USBM Report Investigation, p 28

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

  • Eberhart RC, Shi Y (1998) Evolving artificial neural networks. In: Proceedings of the international conference on neural networks and brain, pp 5–13

  • Eberhart RC, Shi Y (2001) Particle swarm optimization: developments, applications and resources. In: IEEE international conference on evolutionary computation, pp 81–86

  • Eberhart RC, Simpson PK, Dobbins RW (1996) Computational intelligence PC tools. Academic Press Professional, Boston

    Google Scholar 

  • Ebrahimi E, Monjezi M, Khalesi MR, Jahed Armaghani D (2015) Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm. Bull Eng Geol Environ. doi:10.1007/s10064-015-0720-2

    Google Scholar 

  • Engelbrecht AP (2007) Computational intelligence: an introduction, 2nd edn. Wiley, New York

    Book  Google Scholar 

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

    Article  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 19:755–770

    Article  Google Scholar 

  • Ghoraba S, Monjezi M, Talebi N, Moghadam MR, Jahed Armaghani D (2015) Prediction of ground vibration caused by blasting operations through a neural network approach: a case study of Gol-E-Gohar Iron Mine, Iran. J Zhejiang Univ Sci A. doi:10.1631/jzus.A1400252

  • Ghosh A, Daemen JK (1983) A simple new blast vibration predictor. In: Proceedings of the 24th US symposium on rock mechanics College Station, Texas, pp 151–161

  • Gordan B, Jahed Armaghani D, Hajihassani M, Monjezi M (2015) Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng Comput. doi:10.1007/s00366-015-0400-7

    Google Scholar 

  • Griffiths MJ, Oates JAH, Lord P (1978) The propagation of sound from quarry blasting. J Sound Vib 60:359–370

    Article  Google Scholar 

  • Gudise VG, Venayagamoorthy GK (2003) Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks. In: Swarm intelligence symposium SIS’03, Proceedings of the 2003 IEEE, pp 110–117

  • Hajihassani M, Jahed Armaghani D, Sohaei H, Tonnizam Mohamad E, Marto A (2014a) Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Appl Acoust 80:57–67

    Article  Google Scholar 

  • Hajihassani M, Jahed Armaghani D, Marto A, Tonnizam Mohamad E (2014b) Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bull Eng Geol Environ. doi:10.1007/s10064-014-0657-x

    Google Scholar 

  • Hopler RB (1998) Blasters’ handbook, 17th edn. International Society of Explosives Engineers, Cleveland

    Google Scholar 

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

    Article  Google Scholar 

  • Hustrulid WA (1999) Blasting principles for open pit mining: general design concepts. Balkema, Rotterdam

    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 56:97–107

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Jahed Armaghani D, Hajihassani M, Yazdani Bejarbaneh B, Marto A, Tonnizam Mohamad E (2014b) Indirect measure of shale shear strength parameters by means of rock index tests through an optimized artificial neural network. Measurement 55:487–498

    Article  Google Scholar 

  • Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, Piscataway, pp 1942–1948

  • Khandelwal M, Kankar PK (2011) Prediction of blast-induced air overpressure using support vector machine. Arab J Geosci 4:427–433

    Article  Google Scholar 

  • Khandelwal M, Singh TN (2005) Prediction of blast induced air overpressure in opencast mine. Noise Vib Worldw 36:7–16

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Konya CJ, Walter EJ (1990) Surface blast design. Prentice Hall, Englewood Cliffs

    Google Scholar 

  • Kosko B (1994) Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. Prentice Hall, New Delhi

    Google Scholar 

  • Kuzu C, Fisne A, Ercelebi SG (2009) Operational and geological parameters in the assessing blast induced airblast-overpressure in quarries. Appl Acoust 70:404–411

    Article  Google Scholar 

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

    Google Scholar 

  • Liou SW, Wang CM, Huang YF (2009) Integrative discovery of multifaceted sequence patterns by frame-relayed search and hybrid PSO-ANN. J Univ Comput Sci 15:742–764

    Google Scholar 

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

  • McKenzine C (1990) Quarry blast monitoring: technical and environmental perspective. Quarry Manag 17:23–29

    Google Scholar 

  • Mendes R, Cortez P, Rocha M, Neves J (2002) Particle swarms for feedforward neural network training. In: Proceedings of the international joint conference on neural networks. IEEE, pp 1895–1899

  • Mohamed MT (2011) Performance of fuzzy logic and artificial neural network in prediction of ground and air vibrations. Int J Rock Mech Min Sci 48:845–851

    Article  Google Scholar 

  • Momeni E, Jahed Armaghani D, Hajihassani M, Mohd Amin MF (2015) Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Measurement 60:50–63

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Monjezi M, Ahmadi M, Sheikhan A, Bahrami M, Salimi AR (2010) Predicting blast-induced ground vibration using various types of neural networks. Soil Dyn Earthq Eng 30:1233–1236

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Monjezi M, Amini Khoshalan H, Yazdian Varjani A (2012) Prediction of flyrock and backbreak in open pit blasting operation: a neurogenetic approach. Arab J Geosci 5:441–448

    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 Comput Appl 22(7):1637–1643

    Article  Google Scholar 

  • Montana DJ, Davis L (1989) Training feedforward neural networks using genetic algorithms. IJCAI 89:762–767

    Google Scholar 

  • Morhard RC (1987) Explosives and rock blasting. Atlas Powder Company, Dallas

    Google Scholar 

  • National Association of Australian State Road Authorities (1983) Explosives in roadworks—a users guide. NAASRA, Sydney

    Google Scholar 

  • New BM (1986) Ground vibration caused by civil engineering works. Transport and Road Research Laboratory Research Report, NO RR 53

  • Ozer U, Karadogan A, Kahriman A, Aksoy M (2011) Bench blasting design based on site-specific attenuation formula in a quarry. Arab J Geosci 6(3):711–721

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Rafig MY, Bugmann G, Easterbrook DJ (2001) Neural network design for engineering applications. Comput Struct 79:1541–1552

    Article  Google Scholar 

  • Raina AK, Murthy VMSR, Soni AK (2014) Flyrock in bench blasting: a comprehensive review. Bull Eng Geol Environ. doi:10.1007/s10064-014-0588-6

    Google Scholar 

  • Rodríguez R, Toraño J, Menéndez M (2007) Prediction of the airblast wave effects near a tunnel advanced by drilling and blasting. Tunn Undergr Space Technol 22:241–251

    Article  Google Scholar 

  • Rodríguez R, Lombardia C, Torno S (2010) Prediction of the air wave due to blasting inside tunnels: approximation to a ‘phonometric curve’. Tunn Undergr Sp Technol 25:483–489

    Article  Google Scholar 

  • Rosenthal MF, Morlock GL (1987) Blasting guidance manual. Office of Surface Mining Reclamation and Enforcement, US Department of the Interior, OSMRE, p 201

  • Roy PP (1993) Putting ground vibration predictors into practice. Colliery Guardian 241:63–67

    Google Scholar 

  • Roy PP (2005) Rock blasting: effects and operations. CRC Press, Boca Raton

    Google Scholar 

  • Segarra P, Domingo JF, López LM, Sanchidrián JA, Ortega MF (2010) Prediction of near field overpressure from quarry blasting. Appl Acoust 71:1169–1176

    Article  Google Scholar 

  • Settles M, Rylander B (2002) Neural network learning using particle swarm optimizers. In: Advances in information science and soft computing, pp 224–226

  • Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Evolutionary computation Proceedings, 1998. IEEE World congress on computational intelligence, the 1998 IEEE international conference, pp 69–73

  • Simpson PK (1990) Artificial neural system: foundation, paradigms applications and implementations. Pergamon, New York

    Google Scholar 

  • Singh DP, Sastry VR (1986) Rock fragmentation by blasting: influence of joint filling material. J Explos Eng 18–27

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

    Article  Google Scholar 

  • Siskind DE, Stachura VJ, Stagg MS, Koop JW (1980) Structure response and damage produced by airblast from surface mining. Report of investigations 8485, United States Bureau of Mines, Washington, DC

  • Socha K, Blum C (2007) An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training. Neural Comput Appl 16:235–247

    Article  Google Scholar 

  • Specht DF (1991) A general regression neural network. Neural Netw IEEE Trans 2:568–576

    Article  Google Scholar 

  • Tonnizam Mohamad E, Hajihassani M, Jahed Armaghani D, Marto A (2012) Simulation of blasting-induced air overpressure by means of artificial neural networks. Int Rev Model Simul 5:2501–2506

    Google Scholar 

  • Tsou D, MacNish C (2003) Adaptive particle swarm optimization for high-dimensional highly convex search spaces. In: Evolutionary computation, 2003. CEC’03. The 2003 congress, vol 2, pp 783–789

  • Van den Bergh F, Engelbrecht AP (2000) Cooperative learning in neural networks using particle swarm optimizers. S Afr Comput J 26:84–90

    Google Scholar 

  • White TJ, Farnfield RA (1993) Computers and blasting. In: Transactions of the Institution of Mining and Metallurgy, p 102

  • Wiss JF, Linehan P (1978) Control of vibration and blast noise from surface coal mining. US Bureau of Mines, Research Report, Washington DC, p 103

  • Yang Y, Zang O (1997) A hierarchical analysis for rock engineering using artificial neural networks. Rock Mech Rock Eng 30:207–222

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to extend their appreciation to the Universiti Teknologi Malaysia for UTM Research University Grant No. 01H88 and for providing the required facilities that made this research possible.

Author information

Authors and Affiliations

  1. Construction Research Alliance, Universiti Teknologi Malaysia, UTM Skudai, 81310, Johor, Malaysia

    Mohsen Hajihassani

  2. Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, UTM Skudai, 81310, Johor, Malaysia

    Danial Jahed Armaghani, Edy Tonnizam Mohamad & Aminaton Marto

  3. Department of Mining, Tarbiat Modares University, 14115-143, Tehran, Iran

    Masoud Monjezi

Authors
  1. Mohsen Hajihassani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Danial Jahed Armaghani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Masoud Monjezi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Edy Tonnizam Mohamad
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Aminaton Marto
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Masoud Monjezi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hajihassani, M., Jahed Armaghani, D., Monjezi, M. et al. Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach. Environ Earth Sci 74, 2799–2817 (2015). https://doi.org/10.1007/s12665-015-4274-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12665-015-4274-1

Keywords

Search

Navigation