Skip to main content
SpringerLink
Log in

Application of fuzzy inference system for prediction of rock fragmentation induced by blasting

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

Abstract

Appropriate prediction of rock fragmentation is a vital task in the blasting operations of open pit mines. Rock fragmentation is affected by various parameters including blast pattern and rock characteristics, causing understanding the process difficult. As such, application of the robust techniques such as artificial intelligence can be utilized in this regard. In this paper, a predictive model was developed to predict rock fragmentation using fuzzy inference system (FIS) in Sarcheshmeh copper mine, Iran. For this purpose, blasting parameters including burden, spacing, hole diameter, Schmidt hammer rebound number, density of joint, powder factor, and stemming length were considered as model inputs to predict rock fragmentation (D80). In addition, by using the same data, a multiple equation was proposed with the help of multiple regression analysis (MRA). Results of coefficient of determination (R 2) between predicted and measured rock fragmentation were computed as 0.922 and 0.738 for FIS and MRA models, respectively. Moreover, root mean square error (RMSE) and variance account for (VAF) FIS model were obtained as 2.423 and 92.195 %, respectively, while these values were achieved for MRA technique as 4.393 and 73.835 %, respectively. Comparison of the performance indices of the predictive models showed the superiority of the FIS model over the regression technique. Results of sensitivity analysis indicated that burden, spacing, and powder factor are the most influential parameters on rock fragmentation.

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

Similar content being viewed by others

References

  • Acaroglu O, Ozdemir L, Asbury B (2008) A fuzzy logic model to predict specific energy requirement for TBM performance prediction. Tunnel Undergr Sp Technol 23:600–608

    Article  Google Scholar 

  • Alvarez Grima M, Bruines PA, Verhoef PNW (2000) Modeling tunnel boring machine performance by neuro-fuzzy methods. Tunnel Undergr Sp Technol 15(3):259–269

    Article  Google Scholar 

  • Aydin A (2004) Fuzzy set approaches to classification of rock masses. Eng Geol 74:227–245

    Article  Google Scholar 

  • Azimi Y, Osanloo M, Aakbarpour-Shirazi M, AghajaniBazzazi A (2010) Prediction of the blastability designation of rock masses using fuzzy sets. Int J Rock Mech Min Sci 47:1126–1140

    Article  Google Scholar 

  • Bahrami A, Monjezi M, Goshtasbi K, Ghazvinian A (2011) Prediction of rock fragmentation due to blasting using artificial neural network. Eng Comput 27:177–181

    Article  Google Scholar 

  • Bascetin A (1999) Optimal equipment selection in open-pit mines. Ph.D. thesis, Istanbul Technical University, Istanbul, [In Turkish]

  • Choobbasti AJ, Farrokhzad F, Barari A (2009) Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran). Arab J Geosci 2(4):311–319

    Article  Google Scholar 

  • Chuang PH (1995) Use of fuzzy sets for evaluating shear strength of soils. Comput Geotech 17(4):425–446

    Article  Google Scholar 

  • Cunningham CVB (1983) The Kuz-Ram model for prediction of fragmentation from blasting. 1st International Symposium on Rock Fragmentation by Blasting, pp 439–453

  • Deb D (2003) Analysis of coal mine roof fall rate using fuzzy reasoning techniques. Int J Rock Mech Min Sci 40:251–257

    Article  Google Scholar 

  • Dubois D, Prade H (2000) Fundamentals of fuzzy sets. Kluwer, Norwell, MA, p 647

    Book  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. doi:10.1007/s10064-015-0720-2

    Google Scholar 

  • Galetakis M, Vasiliou A (2010) Selective mining of multiple-layer lignite deposits. A fuzzy approach. Expert Syst Appl 37:4266–4275

    Article  Google Scholar 

  • Gligoric Z, Beljic C, Simeunovic V (2010) Shaft location selection at deep multiple orebody deposit by using fuzzy TOPSIS method and network optimization. Expert Syst Appl 37:1408–1418

    Article  Google Scholar 

  • Gokay MK (1998) Fuzzy logic usage in rock mass classifications. J Chamber Min Eng Turkey 37(4):3–11

    Google Scholar 

  • Gokceoglu C (2002) A fuzzy triangular chart to predict the uniaxial compressive strength of the Ankara agglomerates from their petrographic composition. Eng Geol 66:39–51

    Article  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 Intel 17:61–72

    Article  Google Scholar 

  • Grima MA (2000) Neuro-fuzzy modeling in engineering geology. A.A. Balkema, Rotterdam

    Google Scholar 

  • 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 

  • Grima MA, Babuska R (1999) Fuzzy model for the prediction of unconined compressive strength of rock samples. Int J Rock Mech Min Sci 36:339–349

    Article  Google Scholar 

  • Hajihassani M, Jahed Armaghani D, Marto A, Tonnizam Mohamad E (2014) 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 

  • Hjelmberg H (1983) Some ideas on how to improve calculations of the fragment size distribution in bench blasting. 1st International Symposium on Rock Fragmentation by Blasting, pp 469–494

  • Iphar M, Goktan RM (2006) An application of fuzzy sets to the diggability index rating method for surface mine equipment selection. Int J Rock Mech Min Sci 43:253–266

    Article  Google Scholar 

  • ISRM (2007) The complete ISRM suggested methods for rock characterization, testing and monitoring. In: Ulusay R, Hudson JA, Ulusay R, Hudson JA (eds) Suggested methods prepared by the Commission on Testing Methods, International Society for Rock Mechanics. ISRM Turkish National Group, Ankara, Turkey, pp 1974–2006

    Google Scholar 

  • Jahed Armaghani D, Tonnizam Mohamad E, Momeni E, Narayanasamy MS, Mohd Amin MF (2014a) An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite. Bull Eng Geol Environ. doi:10.1007/s10064-014-0687-4

    Google Scholar 

  • Jahed Armaghani D, Hajihassani M, Tonnizam Mohamad E, Marto A, Noorani SA (2014b) 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 (2014c) 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 

  • Jang RJS, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing. Prentice-Hall, Upper Saddle River

    Google Scholar 

  • Jiang YM, Park DW, Deb D, Sanford R (1997) Application of fuzzy set theory in the evaluation of roof categories in longwall mining. Min Eng 49(3):53–57

    Google Scholar 

  • Kayabasi A, Gokceoglu C, Ercanoglu M (2003) Estimating the deformation modulus of rock masses: a comparative study. Int J Rock Mech Min Sci 40:55–63

    Article  Google Scholar 

  • Klose D (2002) Fuzzy rule-based expert system for short-range seismic prediction. Comput Geosci 28:377–386

    Article  Google Scholar 

  • Kuznetsov VM (1973) The mean diameter of the fragments formed by blasting rock. Sov Min Sci 9(2):144–148

    Article  Google Scholar 

  • Li WX, Liu L, Dai LF (2010) Fuzzy probability measures (FPM) based non-symmetric membership function: engineering examples of ground subsidence due to underground mining. Eng Appl Artif Intel 23:420–431

    Article  Google Scholar 

  • Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis of fuzzy controllers. Int J Man Mach Stud 7:1–13

    Article  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. Scic World J Article ID 643715

  • Michaux S, Djordjevic N (2005) Influence of explosive energy on the strength of the rock fragments and SAG mill throughput. Miner Eng 18:439–448

    Article  Google Scholar 

  • Mishnaevsky JR, Schmauder S (1996) Analysis of rock fragmentation with the use of the theory of fuzzy sets. In: Barla, editor. Proceedings of the Eurock 96:735–40

  • Momeni E, Nazir R, Jahed Armaghani D, Maizir H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement 57:122–131

    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, Rezaei M, Yazdian Varjani A (2009) Prediction of rock fragmentation due to blasting in Gol-E-Gohar iron mine using fuzzy logic. Int J Rock Mech Min Sci 46:1273–1280

    Article  Google Scholar 

  • Monjezi M, Rezaei M, Yazdian A (2010a) Prediction of backbreak in open-pit blasting using fuzzy set theory. Expert Syst Appl 37:2637–264

    Article  Google Scholar 

  • Monjezi M, Bahrami A, Yazdian Varjani A (2010b) Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks. Int J Rock Mech Min Sci 47(3):476–480

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Morin AM, Ficarazzo F (2006) Monte Carlo simulation as a tool to predict blasting fragmentation based on the Kuz–Ram model. Comput Geosci 32:352–359

    Article  Google Scholar 

  • Nefeslioglu HA, Gokceoglu C, Sonmez H (2006) Indirect determination of weighted joint density (wJd) by empirical and fuzzy models: Supren (Eskisehir, Turkey) marbles. Eng Geol 85:251–269

    Article  Google Scholar 

  • Nguyen VU, Ashworth E (1985) Rock mass classification by fuzzy sets. In: Proceedings of the 26th US symposium on rock mechanics, Rapid City, 937–45

  • Rezaei M, Monjezi M, Yazdian Varjani A (2011) Development of a fuzzy model to predict flyrock in surface mining. Safety Sci 49(2):298–305

    Article  Google Scholar 

  • Roy PP, Dhar BB (1996) Fragmentation analyzing scale—a new tool for breakage assessment. Proceedings 5th International Symposium on Rock Fragmentation by blasting-FRAGBLAST 5, Balkema, Rotterdam

  • Sakellariou MG, Ferentinou MD (2005) A study of slope stability prediction using neural networks. Geotech Geol Eng 23(4):419–445

    Article  Google Scholar 

  • Sayadi A, Monjezi M, Talebi N, Khandelwal M (2013) A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak. J Rock Mech Geotech Eng 5(4):318–324

    Article  Google Scholar 

  • Sonmez H, Gokceoglu C, Ulusay R (2003) An application of fuzzy sets to the Geological Strength Index (GSI) system used in rock engineering. Eng Appl Artif Intel 16:251–269

    Article  Google Scholar 

  • Stagg MS, Otterness RE, Siskind DE (1992) Effects of blasting practices on fragmentation. Proceedings of the 33rd US. Symposium on Rock Mechanics, pp 313–322

  • Thornton D, Kanchibotla SS, Brunton I (2002) Modelling the impact of rock mass and blast design variation on blast fragmentation. Int J Blast Fragment 6(2):169–1168

    Google Scholar 

  • Tonnizam Mohamad E, Jahed Armaghani D, Noorani SA, Saad R, Alvi Abad NK (2012) Prediction of flyrock in boulder blasting using artificial neural network. Electr J Geotech Eng 17:2585–2595

    Google Scholar 

  • Tonnizam Mohamad E, Jahed Armaghani D, Momeni E, Alavi Nezhad Khalil Abad SV (2014) Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bull Eng Geol Environ. doi:10.1007/s10064-014-0638-0

    Google Scholar 

  • Topcu IB, Sarıdemir M (2008) Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Comput Mat Sci 41:305–311

    Article  Google Scholar 

  • Torabi SR, Shirazi H, Hajali H, Monjezi M (2013) Study of the influence of geotechnical parameters on the TBM performance in Tehran–Shomal highway project using ANN and SPSS. Arab J Geosci 6(4):1215–1227

    Article  Google Scholar 

  • Wu C, Hao H, Zhou Y (1999) Fuzzy-random probabilistic analysis of rock mass responses to explosive loads. Comput Geotech 25:205–225

    Article  Google Scholar 

  • Yagiz S, Gokceoglu C (2010) Application of fuzzy inference system and nonlinear regression models for predicting rock brittleness. Expert Syst Appl 37:2265–2272

    Article  Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  Google Scholar 

  • Zheming X, Mohanty B, Heping Z (2007) Numerical investigation of blasting-induced crack initiation and propagation in rocks. Int J Rock Mech Min Sci 44:412–24

    Article  Google Scholar 

  • Zheming Z, Heping X, Mohanty B (2008) Numerical investigation of blasting-induced damage in cylindrical rocks. Int J Rock Mech Min Sci 45:111–21

    Article  Google Scholar 

  • Zimmermann HJ (1992) Fuzzy set theory and its applications. Kluwer, Boston

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mining, Faculty of Engineering, Tarbiat Modares University, Tehran, 14115-143, Iran

    Samira Shams, Masoud Monjezi & Vahid Johari Majd

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

    Danial Jahed Armaghani

Authors
  1. Samira Shams
    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. Vahid Johari Majd
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Danial Jahed Armaghani
    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

Shams, S., Monjezi, M., Majd, V.J. et al. Application of fuzzy inference system for prediction of rock fragmentation induced by blasting. Arab J Geosci 8, 10819–10832 (2015). https://doi.org/10.1007/s12517-015-1952-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12517-015-1952-y

Keywords

Search

Navigation