Skip to main content
SpringerLink
Log in

Concrete Compressive Strength Prediction Using Neural Networks Based on Non-destructive Tests and a Self-calibrated Response Surface Methodology

  • Published:
Journal of Nondestructive Evaluation Aims and scope Submit manuscript

Abstract

An artificial neural network (ANN) model and response surface methodology (RSM) were established to estimate the compressive strength of concrete by using the combination of three non-destructive tests (NDT); rebound number, pulse velocity tests and resistance surface. These techniques are utilized in an attempt to increase the reliability of the non-destructive tests in detecting the strength of concrete. These methods were trained using a set of different mixes and at different ages of concrete specimens. In this case, 180 experimental specimens were conducted and their data are published. Then, different neural network topologies and algorithms besides RSM were examined using the given data. The published models are for two combination including the combination of UPV and RN and the combination UPV, RN and SR. The results show that the accuracy of the published models are increased by aging. In addition, it is showed that RSM don’t need calibration process, while its accuracy is enough. Hence, RSM is a promising method to conduct on NDTs and compressive strength prediction, while ANN needs to perform many times to find the best accuracy.

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

Similar content being viewed by others

References

  1. Beshr, H., Almusallam, A., Maslehuddin, M.: Effect of coarse aggregate quality on the mechanical properties of high strength concrete. Constr. Build. Mater. 17(2), 97–103 (2003)

    Article  Google Scholar 

  2. Neville, A.M.: Properties of Concrete. Wiley, New York (2005)

    MATH  Google Scholar 

  3. Ali-Benyahia, K., Sbartaï, Z.-M., Breysse, D., Ghrici, M., Kenai, S.: Improvement of nondestructive assessment of on-site concrete strength: influence of the selection process of cores location on the assessment quality for single and combined NDT techniques. Constr. Build. Mater. 195, 613–622 (2019)

    Article  Google Scholar 

  4. In Place Methods for Determination of Strength of Concrete; ACI Manual of Concrete Practice, Part 2: Construction Practices and Inspection Pavements, ACI 228.1R-989, Detroit, MI, 1994, p. 25.

  5. Jones, R.: Testing of concrete by ultrasonic-pulse technique. In: Highway Research Board Proceedings (1953).

  6. Kewalramani, M.A., Gupta, R.: Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks. Autom. Constr. 15(3), 374–379 (2006)

    Article  Google Scholar 

  7. Ferreira, R.M., Jalali, S.: NDT measurements for the prediction of 28-day compressive strength. NDT E Int. 43(2), 55–61 (2010)

    Article  Google Scholar 

  8. Poorarbabi, A.: Conversion factors between non-destructive tests of cubic and cylindrical concrete specimens. AUT J. Civil Eng. (2020). https://doi.org/10.22060/ajce.2020.17274.5624

    Article  Google Scholar 

  9. Karahan, S., Büyüksaraç, A., Işık, E.: The relationship between concrete strengths obtained by destructive and non-destructive methods. Iran. J. Sci. Technol. Trans. Civil Eng. (2020). https://doi.org/10.1007/s40996-019-00334-3

    Article  Google Scholar 

  10. Yucel, M., Namlı, E.: High performance concrete (HPC) compressive strength prediction with advanced machine learning methods: combinations of machine learning algorithms with bagging, rotation forest, and additive regression. In: Cioffi, R. (ed.) Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering, pp. 117–138. IGI Global, Pennsylvania (2020)

    Google Scholar 

  11. Chitti, A.N.: Assess Material Properties of Concrete Using Combined Ndt Methods (2019).

  12. Qasrawi, H.Y.: Concrete strength by combined nondestructive methods. Simply and reliably predicted. Cem. Concr. Res. 30, 739–746 (2000)

    Article  Google Scholar 

  13. Yilmaz, N.G., Goktan, R.: Comparison and combination of two NDT methods with implications for compressive strength evaluation of selected masonry and building stones. Bull. Eng. Geol. Env. 78(6), 4493–4503 (2019)

    Article  Google Scholar 

  14. Sai, G.J., Singh, V.P.: Prediction of compressive strength using support vector regression. In: MENDEL, pp. 51–56 (2019)

  15. Breysse, D.: Nondestructive evaluation of concrete strength: an historical review and a new perspective by combining NDT methods. Constr. Build. Mater. 33, 139–163 (2012)

    Article  Google Scholar 

  16. Selvaraj, S., Sivaraman, S.: Prediction model for optimized self-compacting concrete with fly ash using response surface method based on fuzzy classification. Neural Comput. Appl. 31(5), 1365–1373 (2019)

    Article  Google Scholar 

  17. Awolusi, T., Oke, O., Akinkurolere, O., Sojobi, A.: Application of response surface methodology: predicting and optimizing the properties of concrete containing steel fibre extracted from waste tires with limestone powder as filler. Case Stud. Constr. Mater. 10, e00212 (2019)

    Google Scholar 

  18. Busari, A., Dahunsi, B., Akinmusuru, J., Loto, T., Ajayi, S.: Response surface analysis of the compressive strength of self-compacting concrete incorporating metakaolin. Adv. Sci. Technol. Res. J. 13(2), 7–13 (2019)

    Article  Google Scholar 

  19. Moodi, Y., Mousavi, S.R., Ghavidel, A., Sohrabi, M.R., Rashki, M.: Using response surface methodology and providing a modified model using whale algorithm for estimating the compressive strength of columns confined with FRP sheets. Constr. Build. Mater. 183, 163–170 (2018)

    Article  Google Scholar 

  20. Hammoudi, A., Moussaceb, K., Belebchouche, C., Dahmoune, F.: Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates. Constr. Build. Mater. 209, 425–436 (2019)

    Article  Google Scholar 

  21. ASTM C 597-83 (Reapproved 1991).: Test for Pulse Velocity Through Concrete. ASTM, USA (1991)

  22. BS 1881: Part 20 3.: Measurement of Velocity of Ultrasonic Pulses in Concrete. BSI, UK (1986)

  23. ASTM C 805–85.: Test for Rebound Number of Hardened Concrete. ASTM, USA (1993)

  24. BS 1881: Part 202, 1986.: Recommendations for Surface Hardness Tests by the Rebound Hammer. BSI, UK (1986)

  25. El Mir, A., Nehme, S.G.: Porosity of self-compacting concrete. Procedia Eng. 123, 145–152 (2015)

    Article  Google Scholar 

  26. ASTM C1202–12.: Standard Test Method for Electrical Indication of Concrete’s Ability to Resist Chloride Ion Penetration. ASTM International, West Conshohocken, PA, 8 pp (2012).

  27. AASHTO T 277: Standard Test Method for Electrical Indication of Concrete’s Ability to Resist Chloride. American Association of State Highway and Transportation Officials, Washington, DC 12 pp. (2007)

  28. No, T.C.S.: Guidebook on Non-destructive Testing of Concrete Structures. Int. Atomic Energy Agency, Vienna (2002)

    Google Scholar 

  29. Ramezanianpour, A.A., Pilvar, A., Mahdikhani, M., Moodi, F.: Practical evaluation of relationship between concrete resistivity, water penetration, rapid chloride penetration and compressive strength. Constr. Build. Mater. 25(5), 2472–2479 (2011)

    Article  Google Scholar 

  30. Bezerra, M.A., Santelli, R.E., Oliveira, E.P., Villar, L.S., Escaleira, L.A.: Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta 76(5), 965–977 (2008)

    Article  Google Scholar 

  31. Ghavidel, A., Mousavi, S.R., Rashki, M.: The effect of FEM mesh density on the failure probability analysis of structures. KSCE J. Civil Eng. 22(7), 2370–2383 (2018)

    Article  Google Scholar 

  32. Babu, D.J., King, P., Kumar, Y.P.: Optimization of Cu(II) biosorption onto sea urchin test using response surface methodology and artificial neural networks. Int. J. Environ. Sci. Technol. 16(4), 1885–1896 (2019)

    Article  Google Scholar 

  33. Myers, R., Montgomery, D., Anderson-cook, C.: Response Surface Methodology. Wiley, Hoboken (2009)

    MATH  Google Scholar 

  34. Demuth, H., Beale, M., Hagan, M.: Neural Network Toolbox. Mathworks, Natick (1994)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Civil Engineering Department, University of Sistan and Baluchestan, P.O. Box 9816745563-161, Zahedan, Iran

    Ali Poorarbabi, Mohammadreza Ghasemi & Mehdi Azhdary Moghaddam

Authors
  1. Ali Poorarbabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammadreza Ghasemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mehdi Azhdary Moghaddam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammadreza Ghasemi.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Poorarbabi, A., Ghasemi, M. & Azhdary Moghaddam, M. Concrete Compressive Strength Prediction Using Neural Networks Based on Non-destructive Tests and a Self-calibrated Response Surface Methodology. J Nondestruct Eval 39, 78 (2020). https://doi.org/10.1007/s10921-020-00718-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10921-020-00718-w

Keywords

Search

Navigation