Abstract
This study addresses the effects of rock characteristics and blasting design parameters on blast-induced vibrations in the Kangal open-pit coal mine, the Tülü open-pit boron mine, the Kırka open-pit boron mine, and the TKI Çan coal mine fields. Distance (m, R) and maximum charge per delay (kg, W), stemming (m, SB), burden (m, B), and S-wave velocities (m/s, Vs) obtained from in situ field measurements have been chosen as input parameters for the adaptive neuro-fuzzy inference system (ANFIS)-based model in order to predict the peak particle velocity values. In the ANFIS model, 521 blasting data sets obtained from four fields have been used (r 2 = 0.57–0.81). The coefficient of ANFIS model is higher than those of the empirical equation (r 2 = 1). These results show that the ANFIS model to predict PPV values has a considerable advantage when compared with the other prediction models.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12665-016-6306-x/MediaObjects/12665_2016_6306_Fig1_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12665-016-6306-x/MediaObjects/12665_2016_6306_Fig2_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12665-016-6306-x/MediaObjects/12665_2016_6306_Fig3_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12665-016-6306-x/MediaObjects/12665_2016_6306_Fig4_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12665-016-6306-x/MediaObjects/12665_2016_6306_Fig5_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12665-016-6306-x/MediaObjects/12665_2016_6306_Fig6_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12665-016-6306-x/MediaObjects/12665_2016_6306_Fig7_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12665-016-6306-x/MediaObjects/12665_2016_6306_Fig8_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12665-016-6306-x/MediaObjects/12665_2016_6306_Fig9_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12665-016-6306-x/MediaObjects/12665_2016_6306_Fig10_HTML.gif)
Similar content being viewed by others
References
Ak H (2006) The investigation of directional changes of the blast-induced ground vibration. Dissertation, Eskisehir Osmangazi University (in Turkish)
Aldaş GGU (2002) Effect of some rock mass properties on blasting induced ground vibration wave characteristics at Orhaneli surface coal mine. Dissertation, Middle East Technical University
Alvarez-Vigil AE, Gonzalez-Nicieza C, Gayarre Lopez F, Alvarez-Fernandez MI (2012) Predicting blasting propagation velocity and vibration frequency using artificial neural network. Int J Rock Mech Min Sci 55:108–116
Ambraseys NR, Hendron AJ (1968) Dynamic behavior of rock masses, rock mechanics in engineering practices. Wiley, London
Amnieh HB, Mozdianfard MR, Siamaki A (2010) Predicting of blasting vibrations in sarcheshmeh copper mine by neural network. Saf Sci 48:319–325
Arpaz E (2000) Monitoring and evaluation of blast induced vibrations in some open-pit mines in Turkey. Dissertation, Cumhuriyet University (in Turkish)
Arpaz E, Uysal Ö, Tola Y, Görgülü K, Çavuş M (2012) Comparison of blast-induced ground vibration predictors in Seyitomer coal mine. In: Proceedings 12th rock mechanics symposium, Bei**g, pp 1161–63
Blair DP, Spathis AT (1982) Attenuation of explosion-generated pulse in rock masses. J Geophys Res 87(5):3885–3892
Buragohaın M (2008) Adaptive network based fuzzy inference system (ANFIS) as a tool for system identification with special emphasis on training data minimization. A thesis submitted in partial fulfilment of the requirements for the degree of doctor of philosophy, Department of Electronics and Communication Engineering Indian Institute of Technology Guwahati, Guwahati, India, pp 781–039
Davies B, Farmer IW, Attewell PB (1964) Ground vibrations from shallow sub-surface blasts. Engineer 217:553–559
Duvall WI, Petkof B (1959) Spherical propagation of explosion generated strain pulses in rock. USBM Report of Investigation 5483-21
Fisne A, Kuzu C, Hüdaverdi T (2011) Prediction of environmental impacts of quarry blasting operation using fuzzy logic. Environ Monit Assess 174:461–470
Geometrics Inc (2009) SeisImager/2D software manual; version 3.3:257
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 151–61
Gupta RN, Roy PP, Bagachi A, Singh B (1987) Dynamic effects in various rock mass and their predictions. J Mines Met Fuels 35(11):455–462
Gupta RN, Roy PP, Singh B (1988) On a blast induced blast vibration predictor for efficient blasting. In: Proceedings of the 22nd international conference of safety in Mines, Bei**g, China, pp 1015–1021
Hagan TN (1973) Rock breakage by explosives. In: International proceedings of the national symposium on rock fragmentation, Adelaide, pp 1–17
Hu X (2003) DB-HReduction: a data preprocessing algorithm for data mining applications. Appl Math Lett 16(6):889–895
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
ISI (1973) Criteria for safety and design of structures subjected to underground blast. Indian Standard Institute, ISI Bull 6922
ISRM (1992) Suggested method for blast vibration monitoring. Int J Rock Mech Min Sci Geo Abs 29:143–156
Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685
Jang JSR, Sun CT (1993) Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE Trans Neural Netw 4(1):156–159
Jang JSR, Sun CT (1995) Neuro-fuzzy modeling and control. Proc IEEE 83(3):378–406
Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing. Prectice-Hall, Upper Saddle River, p 614
Jimeno CL, Jimeno EL, Carcedo FJA (1995) Drilling and blasting of rocks. AA Balkema Publishers, Brookfield-Rotterdam, p 390
Kamali M, Ataei M (2010) Prediction of blast induced ground vibrations in Karoun III. Power plant and dam: a neural network. J South Afr Inst Min Metall 110:481–490
Khandelwal M, Singh TN (2006) Prediction of blast induced ground vibrations and frequency in opencast mine: a neural network approach. J Sound Vib 289(4–5):711–725
Khandelwal M, Singh TN (2009) Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46:1214–1222
Langefors U, Kihlström B (1978) The modern technique of rock blasting, 3rd edn. Wiley, New York, p 438
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
Mohamed MT (2011) Performance of fuzzy logic and artificial neural network in prediction of ground and air vibrations. J Eng Sci Assiut Univ 39(2):425–444
Monjezi M, Amiri H, Farrokhi A, Goshtasbi K (2010) Prediction of rock fragmentation due to blasting in sarcheshmeh copper mine using artificial neural networks. Geotech Geol Eng 28:423–430
Nicholls HR, Johnson CF, Duvall WI (1971) Blasting vibrations and their effects on structures. United States Department of Interior, USBM Bulletin 656
Pal RP (2005) Rock blasting. Oxford, IBH Publishing, New Delhi
Park CB, Miller RD, **a J (1999) Multichannel analysis of surface waves (MASW). Geophysics 64(3):800–808
Roy PP (1991) Vibration control in an opencast mine based on improved blast vibration predictors. Min Sci Technol 12:157–165
Sattler KU, Schallehn E (2001) A data preparation framework based on a multidatabase language. In: Proceedings of the international symposium on database engineering and applications, pp 219–228
Singh TN, Dontha LK, Bhardwaj V (2008) Study into blast vibration and frequency using ANFIS and MVRA. Min Technol 117:3
Taghavifar H, Khalilarya S, Jafarmadar S (2015) Adaptive neuro-fuzzy system (ANFIS) based appraisal of accumulated heat from hydrogenfueled engine. Int J Hydrog Energy 2015:1–3
Tatjana V-S, Marina V, Toni M, Vladimir T (2015) An ANFIS model of quality of experience prediction in education. Appl Soft Comput 34:129–138
Thoukalas LH, Uhrig RE (1996) Fuzzy and neural approaches in engineering. Wiley, New York
Tsukamoto Y (1979) An approach to fuzzy reasoning method. In: Gupta MM, Ragade RK, Yager RR (eds) Advances in fuzzy set theory and applications. Elsevier, North-Holland, Amsterdam, pp 137–149
Wiss JF, Linehan PW (1978) Control of vibration and air noise from surface coal mines. III. US Bureau of Mines Report, OFR 103(3)-79, p 623
Acknowledgements
This study is supported by TÜBİTAK (The Science and Technological Research Council of Turkey) Project No. 110M294. The authors would also like to thank the staff of the Electricity Generation Company, Demir Export, Eti Mine, and TKİ for their assistance during the field work.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Koçaslan, A., Yüksek, A.G., Görgülü, K. et al. Evaluation of blast-induced ground vibrations in open-pit mines by using adaptive neuro-fuzzy inference systems. Environ Earth Sci 76, 57 (2017). https://doi.org/10.1007/s12665-016-6306-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12665-016-6306-x