Coupled geomechanical classification and multivariate statistical analysis approach for the optimization of blasting rock boulders


The prediction of blasting rock boulder in discontinuous rock is crucial for the optimization of blasting operations. Therefore, it is necessary to understand the role played by the geological features of a rock mass in controlling oversize fragments. This research is the result of coupled use of multivariate analysis methods and geomechanical indexes in order to identify the main rock mass and blast parameters that affect directly the oversize boulder production during blasting operations. The aggregate quarries selected for this study belong to the Eocene and the Jurassic rock in Tunisia. Diverse techniques were used as atomic absorption, XR diffraction, microscopic study, mechanical test, and scanning electron microscopy image to identify rock matrix. The methodology established by cluster analysis generated from mechanical classification as rock quality designation, rock mass rating, Q-Barton index, and strength index makes possible to classify the studied rock into three classes. A principal component analysis method developed in XLSAT 2018 has been performed on various blast design parameters to illustrate the relation between blasting and rock parameter. For the prediction of oversize fragment resulting from the blasts, a specific formula for every quarry was generated by statistical method. The proposed formulas can be considered as sufficient with an accuracy of more than 80% of the blasted rock after model testing compared with precise boulder percent in twenty blasts.

This is a preview of subscription content, to check access.

US$ 39.95

Learn more about 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



rock quality designation


rock mass rating


strength index

Q :



stress reduction factor


main component analysis

H (m):


( R left(frac{E}{B}right) ) :




Pr (%):



fracture density

Dip (m):


Øf (mm):

hole diameter

Nb° T:

hole number

Bld (%):

boulder percent (%)

E/Øf :

spacing/hole diameter

fract D.:

fracture density

Coupling coeff:

coefficient coupling

Rc (MPa, bar):

uniaxial compressive test

P charg cl (kg):

weight column charge

P charg Pd (kg):

weight bottom charge


total blasting material


stored number

B (m):


E (m):


M (m2):


Ø Cart (mm):

cartridge diameter

CP or Qs (g/t):

specific consumption

( R left(frac{mathrm{ch}mathrm{p}}{mathrm{ch} T}right) ) :

bottom charge/ total charge


  1. Abu Bakar MZ, Tariq SM, Hayat MB, Zahoor MK, Khan MU (2013) Influence of geological discontinuities upon fragmentation by blasting. Pakistan J of Science 65:414–419

    Google Scholar 

  2. Bakhtavar E, Khoshrou H, Badroddin M (2014) Using dimensional-regression analysis to predict the mean particle size of fragmentation by blasting at the Sungun copper mine. Arab J Geosci 8:2111–2120

    Article  Google Scholar 

  3. Bieniawski ZT (1973) Engineering classification of jointed rock masses. Trans S Africain Inst Civil Engrs 15:335–344

    Google Scholar 

  4. Choquette PW, Pry LC (1970) Geologic nomenclature and classification of porosity in sedimentary carbonate. AAPG Bull 54:207–250

    Google Scholar 

  5. Deere DU, Deere DW (1988) Rock quality designation (RQD) index in practice. In: Rock classification systems for engineering purposes, ASTM special publication 984, pp 91–10

  6. Dhekne P Y,Pradhan M, Ravi K, Mishra R and Jade (2017) Boulder prediction in rock blasting using artificial neural network. ARPN J Eng Appl Sci 12:1–15

  7. Dhekne P, Pradhan M, Krishnarao Jade R (2016) Assessment of the effect of blast hole diameter on the number of oversize boulders using ANN model. J Inst Eng 97:21–31

    Google Scholar 

  8. Dunham RL (1962) Classification of carbonate rocks according to depositional texture. Memoir American Association Petroleum Geologist 1:108–121

    Google Scholar 

  9. Hamdi E., Audiguier M, du Mouza J et Fjäder K (2003) Blast induced micro cracks assessment in muck pile blocks: P-waves velocity and porosity measurements. EFEE 2nd World conf. on Explosives and blasting, pp 10-12

  10. Hamdi E, du Mouza J (2005) A methodology for rock mass characterization and classification to improve blast results. Int J Rock Mech Min Sci 42(2):177–194

    Article  Google Scholar 

  11. Hamdi E, Lafhaj Z (2013) Microcracking based rock classification using ultrasonic and porosity parameters and multivariate analysis methods. J Eng Geol 167:27–36

    Article  Google Scholar 

  12. Ezzeiri S, Hamdi E (2018) Characterization of limestone rocks to improve aggregate quality control. International Society for Rock Mechanics and Rock Engineering, 1-12

  13. Ghiasi M, Askarnejad N, Dindarloo SR, Shamsoddini H (2016) Prediction of blast boulders in open pit mines via multiple regression and artificial neural networks. Int J Min Sci Technol 26:183–186

    Article  Google Scholar 

  14. Grobler HP (2010) Using electronic detonators to improve all-round blasting performances. International J for Blasting and Fragmentation Vol 7:1–12

    Google Scholar 

  15. Hoek E (1994) Strength of rock and rock masses. Int Soc Rock Mech News Jl, Vol.2, N°2, pp 4–16

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

    Article  Google Scholar 

  17. Ismail MA, Gozon JS (2007) Effects of discontinuities on fragmentation by blasting. Int J Surf Min Reclam Environ 1:21–25

  18. ISRM (1981) Rock characterization suggested method. Testing and Monitoring, London

  19. Khandelwal M, Saadat M (2015) A dimensional analysis approach to study blast-induced ground vibration. J Rock Mechanics Rock Eng 48:727–735

    Article  Google Scholar 

  20. Kulatilake P.H.S.W, Wang L, Tang H, Liang Y(2011) Evaluation of rock slope stability for Yujian River dam site by kinematic and block theory analyse. J Comput Geotech, 1–16

  21. Lyana K, Hareyani Z, Kamar Shah A, Mohd Hazizan MH (2015) Effect of geological condition on degree of fragmentation in a Simpang Pulai marble quarry. 19:694–701

  22. Mohammadnejad T, Khoei AR (2013) An extended finite element method for hydraulic fracture propagation in deformable porous media with the cohesive crack model. J Finite Elem Anal Des 73:77–95

    Article  Google Scholar 

  23. Müller B (2009) Adapting blasting technologies to the characteristics of rock masses in order to improve blasting results and reduce blasting vibrations. Fragblast 1:361–378

  24. Palmaström A (1982) The volumetric joint count-a useful and simple measure of the degree of rock mass jointing. IAEG Congress, New Delhi, pp 221–228

    Google Scholar 

  25. Rjeveski V (1978) Processus des travaux miniers à ciel ouvert. Nedra, Moscou

    Google Scholar 

  26. Roy PP, Sawmliana C, Singh RK, Chakunde VK (2013) Effective blasting using mixture of ammonium nitrate, fuel oil, sawdust and used oil at limestone mine. J Min Technol Transac Instit Min Metall 121:46–51

    Google Scholar 

  27. Roy S K, Paswan R, Sarim Md (2017) Geological discontinuities, blast vibration and fragmentation control – a case study, 7th Asian Mining Congress & IME 2017, pp 315–323

  28. Ruhland R (1973) Méthode d’étude de la fracturation naturelle de roches associées à divers modèles structuraux. Geol.soc.Bull. Vol. 26:91–113

    Google Scholar 

  29. Siddiqui FI, Ali Shah SM, Behan MY (2009) Measurement of size distribution of blasted rock using digital image processing. J KAU: Engineering Sciences Journal 20:81–93

  30. Singh PK, Roy MP, Paswan RK, Sarim MD, Kumar S, Ranjan R (2015) Rock fragmentation control in opencast blasting. J Rock Mech Geotech Eng 8:225–237

    Article  Google Scholar 

  31. Singh PK, Sirveiya AK, Babu KN, Roy MP, Singh CV (2007) Evolution of effective charge weight per delay for prediction of ground vibrations generated from blasting in a limestone mine. Int J Min Reclam Environ 20:4–19

    Article  Google Scholar 

  32. Singh SP, Narendrula R, Duffy D (2005) Influence of blasted muck on the productivity of the loading equipment. In: Proceedings of the 3rd EFEE conference on explosives and blasting, pp 347-353

  33. Snoussi G, Essaieb H, Lafhaj Z (2014) Multivariate analysis methods based methodology for rock microcracking characterization. Geotech Geol Eng 32:973–986

  34. Srivastava A, Yang X, Sarrafzadeh M (2002) Optimal energy aware clustering in sensor networks. J Sensors Mol Divers Preserv Int 2:258–269

  35. Vivek K, Roy H, Mishra AK, Paswan RK, Panda D, Singh PK (2018) Multivariate statistical analysis approach for prediction of blast-induced ground vibration. Arab J Geosci 11:1–11

    Article  Google Scholar 

  36. Voulgarakis AG, Michalakopoulos TN, Panagiotou GN (2016) The minimum response time in rock blasting: a dimensional analysis of full-scale experimental data. Min Technol Section A 125:242–248

Download references

Author information

  1. Laboratory of Mineral Resources and the Environment, Department of Geology, Tunis El Manar University, Academic Campus, 2092, Tunis, Tunisia

    Sofien Ben Messaoud & Mohamed Gaied

  2. Laboratory of Geotechnical Engineering and Georisks, National Engineering School at Tunis, Tunis El Manar University, BP 37 The Belvédère, 1002, Tunis, Tunisia

    Essaieb Hamdi

Additional information

Responsible editor: Murat Karakus

Rights and permissions

Reprints and Permissions

About this article


Leave a Reply

Your email address will not be published. Required fields are marked *