Environmentally sensitive blasting design based on risk analysis by using artificial neural networks

  • Umit OzerEmail author
  • Abdulkadir Karadogan
  • Meric Can Ozyurt
  • Ulku Kalayci Sahinoglu
  • Zeynep Sertabipoglu
Original Paper


The aim of this study is to develop an artificial neural network (ANN) which can design an environmentally sensitive blasting project and predict peak particle velocity (PPV) for an urban foundation excavation project with risk elements having different vibration-carrying capacities. In the study area, there are risk factors with different vibration capacities such as revetment systems and ongoing and completed reinforced concrete structures. It is mandatory to use the PPV limit values specified in the Turkish norm when assessing damage to the completed buildings. However, the vibration-carrying capacities of all structures in Turkish norm are accepted as the same. This situation may pose a risk to the buildings under construction. This risk has been avoided by using Jimeno et al. approach, where PPV limit values vary according to the type of buildings and the concrete setting times. The evaluation of different risk factors according to different damage criteria has made blasting excavation activities a complicated problem. In order to solve this problem, an ANN was used which knows the damage criteria that should be based on the element of risk and the geological and rock properties of the site. At the same time, the ANN can predict the blasting designs to be applied according to the element of risk, concrete setting times, and the distance to the risk point and can estimate the PPV to be occurred. Site-specific vibration propagation equation has been obtained as a result of the test shots. Using this equation, the maximum charge amounts per delay were calculated in different regions of the field, and different designs were proposed accordingly. ANN was trained with the samples representing the test shots, and the proposed designs and the performance were evaluated. The outputs of the ANN model, which can learn the problem and provide high accuracy estimates, were applied at 37 shots. PPV values measured at 37 shots were below the damage limits. This shows that the network is capable of the geological and rock properties of the site, and outputs that can represent vibration-carrying capacities of elements of risk. As a result, it is understood that ANN was found to be an effective tool in solving complex problems such as in this study.


ANN PPV Risk analysis Urban area Blasting design 



The authors would like to thank Istanbul University—Cerrahpasa Engineering Faculty, Executive Secretariat of Scientific Research Projects of Istanbul University-Cerrahpasa, DRK Logistics Services Org. and Transportation Inc., and GYA Real Estate Investment and Construction Inc.

Funding information

This work was financially supported by the Executive Secretariat of Scientific Research Projects of Istanbul University-Cerrahpasa (codes of projects 37735, 7023, 10296, 8765, 13293, 21628, 25573) and Engineering Faculty Revolving Fund (project code, 22.12.2016/462098).


  1. Amnieh BH, Mozdianfard MR, Siamaki A (2010) Predicting of blasting vibrations in Sarcheshmeh copper mine by neural network. Saf Sci 48(3):319–325CrossRefGoogle Scholar
  2. Amnieh BH, Siamaki A, Soltanii S (2012) Pattern of blasting pattern in proportion to the peak particle velocity (ppv): artificial neural networks approach. Saf Sci 50(9):1913–1916CrossRefGoogle Scholar
  3. Anon (2010) Çevresel Gürültünün Değerlendirilmesi ve Yönetimi Yönetmeliği. Regulation on Assessment and Management on Environmental Noise. Republic of Turkey Ministry of Environmental and Urbanization. Official newspaper vol, Eng, p 27601Google Scholar
  4. Armaghani DJ, Hajihassani M, Mohamad ET, Marto A, Noorani SA (2014) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab J Geosci 7:5383–5396. CrossRefGoogle Scholar
  5. Askin D, Iskender I, Mamizadeh A (2011) Dry type transformer winding thermal analysis using different neural networks methods. J Fac Eng Arch Gazi Univ 26(4):905–913Google Scholar
  6. Dehghani H, Ataee-Pour M (2011) Development of a model to predict peak particle velocity in a blasting operation. Int J Rock Mech Min Sci 48:51–58CrossRefGoogle Scholar
  7. Dindarloo SR (2015) Prediction of blast-induced ground vibrations via genetic programming. Int J Min Sci Technol 25:1011–1015CrossRefGoogle Scholar
  8. Dowding CH (1985) Blast vibration monitoring and control. Prentice-Hall, Inc., Englewood cliffs, NJ. In: USAGoogle Scholar
  9. Foo JS, Ghosh PS (2002) Artificial neural networks modelling of partial discharge parameters for transformer oil diagnosis. Annual Report Conference on Electrical Insulation and Dielectric, Phenomena, Malaysia pp. 470–473Google Scholar
  10. Foresee FD, Hagan MT (1999) Gauss–Newton approximation to Bayesian regularization. Proc. of. Int Conference on Neural Networks ICNN’97:1930–1935Google Scholar
  11. Ghasemi E, Amini H, Ataei M, Khalokakaei R (2014) Application of soft computing in predicting rock fragmentation to reduce environmental blasting side effects. Arab J Geosci 7:505–505. CrossRefGoogle Scholar
  12. Gorgulu K, Arpaz E, Uysal Ö, Duruturk YS, Yuksek AG, Kocaslan A, Dilmac MK (2015) Investigation of the effects of blasting design parameters and rock properties on blast-induced ground vibrations. Arab J Geosci 8:4269–4278. CrossRefGoogle Scholar
  13. Hagan MT, Menhaj M (1994) Training feedforward networks with the Marquardt algorithm. IEEE Neural Networks IEEE 5(6):989–993CrossRefGoogle Scholar
  14. 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. Int J Geosci 56(1):97–107Google Scholar
  15. Jimeno CL, Jimeno EL, Carcedo FJA (1995) Drilling and blasting of rocks. AA Balkema, Rotterdam, BrookfieldGoogle Scholar
  16. Kardeşler Drilling (2016) Geological and geotechnical investigation report of Istanbul Province, Eyüp District Güzeltepe (Alibeyköy) neighborhood, 54 parcel. September 2016 (in Turkish)Google Scholar
  17. Kaur H, Salaria DS (2013) Bayesian regularization based neural networks tool for software effort estimation. Glob J Comput Sci Technol: Neural Artif Intell 13(I.2):44–50Google Scholar
  18. Kayri M (2016) Predictive abilities of Bayesian regularization and Levenberg–Marquardt algorithms in artificial neural networks: a comparative empirical study on social data. Math Comput Appl MDPI 21(20):1–11. CrossRefGoogle Scholar
  19. Khandelwal M, Singh TN, Kumar S (2005) Prediction of blast induced ground vibration in opencast mine by artificial neural network. Ind Min Eng J 44:9–23Google Scholar
  20. Khandelwal M, Singh TN (2006) Prediction of blast induced ground vibration and frequency in open cast mine: a neural networks approach. J Sound Vib 289:711–725CrossRefGoogle Scholar
  21. Langefors U, Kihlström B (1976) The modern technique of rock blasting, 3rd edn. John Wiley and Sons, New YorkGoogle Scholar
  22. Levenberg K (1944) A method for the solution of certain non-linear problems in least squares. Q Appl Math 2:164–168CrossRefGoogle Scholar
  23. Mackay DJC (1991) Bayesian methods for adaptive models. Dissertation, California Institute of TechnologyGoogle Scholar
  24. Marquardt D (1963) An algorithm for least-squares estimation of nonlinear parameters. SIAM J Appl Math 11(2):431–441. CrossRefGoogle Scholar
  25. Matlab Documentation (2017) Bayesian regularization backpropagation. : Accessed 05 May 2017
  26. Mohammad MT (2009) Artificial neural networks for prediction and control of blasting vibration in Assiut (Egypt) limestone quarry. Int J Rock Mech Min Sci 46:426–431CrossRefGoogle Scholar
  27. Monjezi M, Bahrami A, Varjani AY, Sayadi AR (2011) Prediction and controlling of flyrock in blasting operation using artificial neural network. Arab J Geosci 4:421–425. CrossRefGoogle Scholar
  28. Monjezi M, Mohamadi HA, Barati B, Khandelwal M (2014) Application of soft computing in predicting rock fragmentation to reduce environmental blasting side effects. Arab J Geosci 7:505–511. CrossRefGoogle Scholar
  29. Ozer U, Karadogan A, Kahriman A, Aksoy M (2011) Bench blasting design based on site-specific attenuation formula in a quarry. Arab J Geosci 6:711–721. CrossRefGoogle Scholar
  30. Ozer U, Karadogan A, Sertabipoglu Z, Sahinoglu KU, Ozyurt MC (2016) Measurement and evaluation of vibration and airblast caused by blasting in basic excavation work in the construction of office and trade center, Istanbul Province, Eyüp District Güzeltepe (Alibeyköy) Neighborhood, 75 Plot, 2 Block, 49 and 54 Parcel. Ist Univ Eng Fac Revolving Fund, Project Date/ Number 22(12):2016/462098 (in Turkish)Google Scholar
  31. Payal A, Rai CS, Reddy BVR (2013) Comparative analysis of Bayesian regularization and Levenberg-Marquardt training algorithm for localization in wireless sensor network. the 15th International Conference on Advanced Communications Technology-ICACT201, pp.191–194Google Scholar
  32. Rojas R (1996) Neural networks: a systematic introduction. Springer-Verlag, Berlin, pp.453
  33. Sawmliana C, Roy Pal P, Singh RK, Singh TN (2007) Blast induced air overpressure and its prediction using artificial neural network. Int J Min Tech 1168(2):41–48CrossRefGoogle Scholar
  34. Sazid M, Singh TN (2013) Two-dimensional dynamic finite element simulation of rock blasting. Arab J Geosci 6:3703–3708. CrossRefGoogle Scholar
  35. Singh TN (2004) Artificial neural networks approach for prediction and control of ground vibrations in mines. Trans Inst Min Metall: Min Tech 113:A251–A257Google Scholar
  36. Singh TN, Dontha LK, Bharadwaj V (2008) A study into blast vibration and frequency using ANFIS and MVRA. Trans Inst Min Metall: Min Tech 117(3):116–121Google Scholar
  37. Singh TN, Kanchan R, Verma AK (2004) Prediction of blast induced ground vibration and frequency using an artificial intelligent technique. International journal of noise and vibration. World-wide 35(11):7–14CrossRefGoogle Scholar
  38. Singh TN, Premkrishnan R (2000) Ground vibrations due to blasting and its environmental impacts. IM and EJ, pp144–149Google Scholar
  39. Singh TN, Singh A, Singh CS (1994) Prediction of ground vibration induced by blasting. Indian Mining Eng J 33:31–34Google Scholar
  40. Singh TN, Singh V (2005) An intelligent approach to prediction and control ground vibration in mines. Geotech Geol Eng 23:249–262. CrossRefGoogle Scholar
  41. Siskind DE, Stagg MS, Kopp JW and Dowding CH (1980) Structure response and damage produced by ground vibration from surface mine blasting. USBM Report of Investigation 8507Google Scholar
  42. Tosun A, Konak G (2015) Determination of specific charge minimizing total unit cost of open pit quarry blasting operations. Arab J Geosci 8:6409–6423i. CrossRefGoogle Scholar
  43. T.R. (2010) Ministry of Environment and urbanization, Environmental Noise Assessment and Management Regulation Number of Official Gazette: 27917Google Scholar
  44. Vas P (1999) Artificial intelligence based electrical machines and drivers. Oxford University Press, New York, p 625Google Scholar
  45. Wang W, Gelder P, Vrijling JK (2007) Comparing Bayesian regularization and cross-validated early stopping for streamflow forecasting with ANNs. Proceedings of the Second International Symposium on Methodology in Hydrology Held in Nanjing, China. IAHS Publ 311:216–221Google Scholar

Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  • Umit Ozer
    • 1
    Email author
  • Abdulkadir Karadogan
    • 1
  • Meric Can Ozyurt
    • 1
  • Ulku Kalayci Sahinoglu
    • 1
  • Zeynep Sertabipoglu
    • 1
  1. 1.Department of Mining Engineering, Faculty of EngineeringIstanbul University—CerrahpasaIstanbulTurkey

Personalised recommendations