Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Ultrasonic velocity as a tool for geotechnical parameters prediction within carbonate rocks aggregates

  • 30 Accesses


Production of rock aggregates is an important industrial activity. Quality estimation of rock aggregates is often performed with standardized mechanical tests which are intended for testing the products, not the original rock material. In fact, conventional tests dealing with mechanical performance of aggregates (abrasion and fragmentation resistance) are laborious tasks. They are time consuming and require tough laboratory procedures. Thus, there is a need for an effective method to estimate the quality of rock aggregates in the early stages of quarry prospection. This work aims to present a non-destructive ultrasonic technique to characterize mechanical strength of carbonate rock aggregates, mainly defined with Los Angeles (L.A.) and Micro-Deval (M.D.E.) measurements. For experimentation, porosity, density, L.A., and M.D.E. coefficients were calculated for 11 carbonate rock samples. Beforehand, ultrasonic measurements were taken on rock samples using longitudinal P wave with a frequency of 55 kHz. Regression analysis indicated that L.A. and M.D.E. coefficients were linearly correlated with ultrasonic velocity. Similar results were shown for porosity and density. Artificial neural networking was performed to establish a predictive model linking porosity density and ultrasonic velocity to L.A. or M.D.E. measurements. For our knowledge, this is the first paper of a correlation between L.A. and M.D.E. coefficients with ultrasonic velocity. Results have indicated the ability of this technique to elaborate an accurate approach for prediction of mechanical performances determined with laborious experiments on rock aggregates. This paper adds to the knowledge about the wide effectiveness of ultrasonic techniques to predict the quality of aggregates and proves its efficacy in estimating their quality in the early stages of its production, in field and in time, which presents economical benefits for rock aggregate industries.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10


  1. Abdelhedi M, Aloui M, Mnif T, Abbes C (2017) Ultrasonic velocity as a tool for mechanical and physical parameters prediction within carbonate rocks. Geomechan Eng 13:371–384. https://doi.org/10.12989/gae.2017.13.3.371

  2. Abdelhedi M, Mnif T, Abbes C (2018) Ultrasonic velocity as a tool for physical and mechanical parameters prediction within cement mortar. Russ J Nondestruct Test 54:345–355. https://doi.org/10.1134/S1061830918050091

  3. Ceryan N, Okkan U, Kesimal A (2013) Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks. Environ Earth Sci 68:807–819. https://doi.org/10.1007/s12665-012-1783-z

  4. Chen X, Schmitt DR, Kessler JA, Evans J, Kofman R (2015) Empirical relations between ultrasonic P-wave velocity, porosity and uniaxial compressive strength. CSEG Rec 40:24–29

  5. Cooley L Jr, James R (2003) Micro-Deval testing of aggregates in the southeast. Transp Res Rec 1837:73–79. https://doi.org/10.3141/1837-08

  6. Eberli GP, Baechle GT, Anselmetti FS, Incze ML (2003) Factors controlling elastic properties in carbonate sediments and rocks. Lead Edge 22:654–660

  7. Erichsen E, Ulvik A, Sævik K (2011) Mechanical degradation of aggregate by the Los Angeles-, the micro-deval-and the Nordic test methods. Rock Mech Rock Eng 44:333–337. https://doi.org/10.1007/s00603-011-0140-y

  8. Gökalp İ, Uz VE, Saltan M (2016) Testing the abrasion resistance of aggregates including by-products by using Micro Deval apparatus with different standard test methods. Constr Build Mater 123:1–7. https://doi.org/10.1016/j.conbuildmat.2016.06.141

  9. Goreham VC, Lake CB (2013) Influence of water on diffusion and porosity parameters of soil–cement materials. Can Geotech J 50(4):351–358

  10. Haddad K, Haddad O, Aggoun S, Kaci S (2017) Correlation between the porosity and ultrasonic pulse velocity of recycled aggregate concrete at different saturation levels. Can J Civ Eng 44(11):911–917

  11. Ham FM, Kostanic I (2001) Principles of neurocomputing for science and engineering. McGraw-Hill Higher Education, New York

  12. Hamdi E, Lafhaj Z (2013) Microcracking based rock classification using ultrasonic and porosity parameters and multivariate analysis methods. Eng Geol 167:27–36. https://doi.org/10.1016/j.enggeo.2013.10.008

  13. Hernández MG, Anaya JJ, Izquierdo MAG, Ullate LG (2002) Application of micromechanics to the characterization of mortar by ultrasound. Ultrasonics 40:217–221. https://doi.org/10.1016/S0041-624X(02)00140-3

  14. Kahraman S, Alber M, Fener M, Gunaydin O (2010) The usability of Cerchar abrasivity index for the prediction of UCS and E of Misis Fault Breccia: regression and artificial neural networks analysis. Expert Syst Appl 37:8750–8756. https://doi.org/10.1016/j.eswa.2010.06.039

  15. Kahraman S, Gunaydin O (2007) Empirical methods to predict the abrasion resistance of rock aggregates. Bull Eng Geol Environ 66:449–455. https://doi.org/10.1007/s10064-007-0093-2

  16. Köhn D, Meier T, Fehr M, De-Nil D, Auras M (2016) Application of 2D elastic Rayleigh waveform inversion to ultrasonic laboratory and field data. Near Surf Geophys 14(5):461–476

  17. Kurtulus C, Bozkurt A, Endes H (2012) Physical and mechanical properties of serpentinized ultrabasic rocks in NW Turkey. Pure Appl Geophys 169:1205–1215. https://doi.org/10.1007/s00024-011-0394-z

  18. Lafhaj Z, Goueygou M (2009) Experimental study on sound and damaged mortar: variation of ultrasonic parameters with porosity. Constr Build Mater 23:953–958. https://doi.org/10.1016/j.conbuildmat.2008.05.012

  19. Lotfi H, Faiz B, Moudden A, Izbaim D, Menou A, Maze G (2010) Characterization of mortars with ultrasonic transducer. Mj Condens Matter 12:2

  20. Madhubabu N, Singh PK, Kainthola A, Mahanta B, Tripathy A, Singh TN (2016) Prediction of compressive strength and elastic modulus of carbonate rocks. Measurement 88:202–213. https://doi.org/10.1016/j.measurement.2016.03.050

  21. Maev RG (2008) Acoustic microscopy: fundamentals and applications. John Wiley & Sons, Darmstadt

  22. Mozumder RA, Laskar AI (2015) Prediction of unconfined compressive strength of geopolymer stabilized clayey soil using artificial neural network. Comput Geotech 69:291–300

  23. Nejad FP, Jaksa MB (2017) Load-settlement behavior modeling of single piles using artificial neural networks and CPT data. Comput Geotech 89:9–21

  24. Nicolas A, Fortin J, Regnet JB, Dimanov A, Guéguen Y (2016) Brittle and semi-brittle behaviours of a carbonate rock: influence of water and temperature. Geophys J Int 206:438–456. https://doi.org/10.1093/gji/ggw154

  25. Ozcelik Y (2011) Predicting Los Angeles abrasion of rocks from some physical and mechanical properties. Sci Res Essays 6:1612–1619. https://doi.org/10.5897/SRE10.1164

  26. Peng S, Zhang J (2007) Engineering geology for underground rocks. Springer Science & Business Media, Berlin

  27. Rafiq MY, Bugmann G, Easterbrook DJ (2001) Neural network design for engineering applications. Comput Struct 79:1541–1552. https://doi.org/10.1016/S0045-7949(01)00039-6

  28. Shakoor A, Brown CL (1996) Development of a quantitative relationship between unconfined compressive strength and Los Angeles abrasion loss for carbonate rocks. Bull Int Assoc Eng Geol 53:97–103. https://doi.org/10.1007/BF02594945

  29. Singh TN, Kanchan R, Saigal K, Verma AK (2004) Prediction of p-wave velocity and anisotropic property of rock using artificial neural network technique. J Sci Ind Res India 63:32–38

  30. Trippi RR, Lee JK (1996) Artificial intelligence in Finance & Investing. IRWIN Professional Publishing, Chicago

  31. Trtnik G, Kavčič F, Turk G (2009) Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks. Ultrasonics 49:53–60. https://doi.org/10.1016/j.ultras.2008.05.001

  32. Vasconcelos G, Lourenço PB, Alves CAS, Pamplona J (2008) Ultrasonic evaluation of the physical and mechanical properties of granites. Ultrasonics 48:453–466. https://doi.org/10.1016/j.ultras.2008.03.008

  33. Vergara L, Miralles R, Gosálbez J et al (2001) NDE ultrasonic methods to characterise the porosity of mortar. NDT&E Int 34:557–562. https://doi.org/10.1016/S0963-8695(01)00020-2

  34. Yasar E, Erdogan Y (2004) Correlating sound velocity with the density, compressive strength and Young’s modulus of carbonate rocks. Int J Rock Mech Min 41:871–875. https://doi.org/10.1016/j.ijrmms.2004.01.012

Download references


Experimental assays were performed in the “Département des Sciences de la Terre” of the “Faculté des Sciences de Sfax, Université de Sfax-Tunisie.”


This work received financial support from the “Ministère de l’Enseignement Supérieur et de la Recherche Scientifique en Tunisie.”

Author information

Correspondence to Mohamed Abdelhedi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information


• Linear relationships between ultrasonic velocities and the M.D.E./L.A. coefficients were established within carbonate rock aggregates.

• Correlations were set relating ultrasonic velocity with porosity and density within rock aggregates.

• Initial investigation of aggregates producing rocks using ultrasonic testing is confirmed.

• Capacity of ultrasonic method to form an accurate model for estimating mechanical parameters of rock aggregates.

Responsible Editor: Zeynal Abiddin Erguler

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Abdelhedi, M., Jabbar, R., Mnif, T. et al. Ultrasonic velocity as a tool for geotechnical parameters prediction within carbonate rocks aggregates. Arab J Geosci 13, 180 (2020). https://doi.org/10.1007/s12517-020-5070-0

Download citation


  • M.D.E.
  • L.A.
  • Artificial neural networking
  • Non-destructive evaluation
  • Aggregates