Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete

  • Faezehossadat Khademi
  • Mahmoud Akbari
  • Sayed Mohammadmehdi Jamal
  • Mehdi Nikoo
Research Article

Abstract

Evaluating the in situ concrete compressive strength by means of cores cut from hardened concrete is acknowledged as the most ordinary method, however, it is very difficult to predict the compressive strength of concrete since it is affected by many factors such as different mix designs, methods of mixing, curing conditions, compaction, etc. In this paper, considering the experimental results, three different models of multiple linear regression model (MLR), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) are established, trained, and tested within the Matlab programming environment for predicting the 28 days compressive strength of concrete with 173 different mix designs. Finally, these three models are compared with each other and resulted in the fact that ANN and ANFIS models enables us to reliably evaluate the compressive strength of concrete with different mix designs, however, multiple linear regression model is not feasible enough in this area because of nonlinear relationship between the concrete mix parameters. Finally, the sensitivity analysis (SA) for two different sets of parameters on the concrete compressive strength prediction are carried out.

Keywords

concrete 28 days compressive strength multiple linear regression artificial neural network ANFIS sensitivity analysis (SA) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Khademi F, Akbari M, Jamal S M. Prediction of compressive strength of concrete by data-driven models. i-manager’s Journal on Civil Engineering, 2015, 5(2): 16–23CrossRefGoogle Scholar
  2. 2.
    Nikoo M, Torabian Moghadam F, Sadowski L. Prediction of concrete compressive strength by evolutionary artificial neural networks. Advances in Materials Science and Engineering, 2015Google Scholar
  3. 3.
    Sobhani J, Najimi M, Pourkhorshidi A R, Parhizkar T. Prediction of the compressive strength of no-slump concrete: a comparative study of regression, neural network and ANFIS models. Construction & Building Materials, 2010, 24(5): 709–718CrossRefGoogle Scholar
  4. 4.
    Sarıdemir M. Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic. Advances in Engineering Software, 2009, 40(9): 920–927CrossRefMATHGoogle Scholar
  5. 5.
    Ramezanianpour A A, Sobhani M, Sobhani J. Application of network-based neuro-fuzzy system for prediction of the strength of high strength concrete, 2004Google Scholar
  6. 6.
    Bal L, Buyle-Bodin F. Artificial neural network for predicting drying shrinkage of concrete. Construction & Building Materials, 2013, 38: 248–254CrossRefGoogle Scholar
  7. 7.
    Atici U. Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network. Expert Systems with Applications, 2011, 38(8): 9609–9618CrossRefGoogle Scholar
  8. 8.
    Sadowski Ł, Nikoo M, Nikoo M. Principal Component analysis combined with a self-organization feature map to determine the pulloff adhesion between concrete layers. Construction & Building Materials, 2015, 78: 386–396CrossRefGoogle Scholar
  9. 9.
    Nikoo M, Zarfam P, Nikoo M. Determining displacement in concrete reinforcement building with using evolutionary artificial neural networks. World Applied Sciences Journal, 2012, 16(12): 1699–1708Google Scholar
  10. 10.
    Amani J, Moeini R. Prediction of shear strength of reinforced concrete beams using adaptive neuro-fuzzy inference system and artificial neural network. Scientia Iranica, 2012, 19(2): 242–248CrossRefGoogle Scholar
  11. 11.
    Sadowski L, Nikoo M. Corrosion current density prediction in reinforced concrete by imperialist competitive algorithm. Neural Computing & Applications, 2014, 25(7–8): 1627–1638Google Scholar
  12. 12.
    Khademi F, Behfarnia K. Evaluation of concrete compressive strength using artificial neural network and multiple linear regression models. Iran University of Science & technology (Elmsford, N.Y.), 2016, 6(3): 423–432Google Scholar
  13. 13.
    Nikoo M, Zarfam P, Sayahpour H. Determination of compressive strength of concrete using self organization feature map (SOFM). Engineering with Computers, 2015, 31(1): 113–121CrossRefGoogle Scholar
  14. 14.
    Vu-Bac N, Lahmer T, Zhang Y, Zhuang X, Rabczuk T. Stochastic predictions of interfacial characteristic of polymeric nanocomposites (PNCs). Composites. Part B, Engineering, 2014, 59: 80–95CrossRefGoogle Scholar
  15. 15.
    Vu-Bac N, Lahmer T, Keitel H, Zhao J, Zhuang X, Rabczuk T. Stochastic predictions of bulk properties of amorphous polyethylene based on molecular dynamics simulations. Mechanics of Materials, 2014, 68: 70–84CrossRefGoogle Scholar
  16. 16.
    Vu-Bac N, Rafiee R, Zhuang X, Lahmer T, Rabczuk T. Uncertainty quantification for multiscale modeling of polymer nanocomposites with correlated parameters. Composites. Part B, Engineering, 2015, 68: 446–464CrossRefGoogle Scholar
  17. 17.
    Khademi F, Jamal, S M. Predicting the 28 Days compressive strength of concrete using artificial neural network. i-Manager’s Journal on Civil Engineering, 2016, 6(2): 1CrossRefGoogle Scholar
  18. 18.
    Sadrmomtazi A, Sobhani J, Mirgozar M A. Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS. Construction & Building Materials, 2013, 42: 205–216CrossRefGoogle Scholar
  19. 19.
    Hoła J, Schabowicz K. Application of artificial neural networks to determine concrete compressive strength based on non–destructive tests. Journal of civil Engineering and Management, 2005, 11(1): 23–32Google Scholar
  20. 20.
    Naderpour H, Kheyroddin A, Amiri G G. Prediction of FRPconfined compressive strength of concrete using artificial neural networks. Composite Structures, 2010, 92(12): 2817–2829CrossRefGoogle Scholar
  21. 21.
    Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators. Neural Networks, 1989, 2(5): 359–366CrossRefMATHGoogle Scholar
  22. 22.
    Jeong D I, Kim Y O. Rainfall-runoff models using artificial neural networks for ensemble streamflow prediction. Hydrological Processes, 2005, 19(19): 3819–3835CrossRefGoogle Scholar
  23. 23.
    Boğa A R, Öztürk M, Topçu İ B. Using ANN and ANFIS to predict the mechanical and chloride permeability properties of concrete containing GGBFS and CNI. Composites. Part B, Engineering, 2013, 45(1): 688–696CrossRefGoogle Scholar
  24. 24.
    Jang J S R, Sun C T. Neuro-fuzzy modeling and control. Proceedings of the IEEE, 1995, 83(3): 378–406CrossRefGoogle Scholar
  25. 25.
    MATLAB and Statistics Toolbox Release. The Math Works, Inc., Natick, Massachusetts, United States, 2014Google Scholar
  26. 26.
    Gandomi A H, Sajedi S, Kiani B, Huang Q. Genetic programming for experimental big data mining: A case study on concrete creep formulation. Automation in Construction, 2016, 70: 89–97CrossRefGoogle Scholar
  27. 27.
    Khademi F, Akbari M, Jamal S M. Measuring compressive strength of puzzolan concrete by ultrasonic pulse velocity method. i-Manager’s Journal on Civil Engineering, 2015, 5(3), 23CrossRefGoogle Scholar
  28. 28.
    Sajedi S, Razavizadeh A, Minaii Z, Ghassemzadeh F, Shekarchi M. A rational method for calculation of restrained shrinkage stresses in repaired concrete members. Concrete Solutions, 2011, 461Google Scholar
  29. 29.
    Sajedi S, Huang Q. Probabilistic prediction model for average bond strength at steel–concrete interface considering corrosion effect. Engineering Structures, 2015, 99: 120–131CrossRefGoogle Scholar
  30. 30.
    Vu-Bac N, Silani M, Lahmer T, Zhuang X, Rabczuk T. A unified framework for stochastic predictions of mechanical properties of polymeric nanocomposites. Computational Materials Science, 2015, 96: 520–535CrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Faezehossadat Khademi
    • 1
  • Mahmoud Akbari
    • 2
  • Sayed Mohammadmehdi Jamal
    • 3
  • Mehdi Nikoo
    • 4
  1. 1.Department of Civil, Architectural and Environmental EngineeringIllinois Institute of TechnologyChicagoUSA
  2. 2.Civil Engineering DepartmentUniversity of KashanKashanIran
  3. 3.Department of Civil EngineeringUniversity of HormozganBandar AbbasIran
  4. 4.Ahvaz BranchIslamic Azad UniversityAhvazIran

Personalised recommendations