Skip to main content
SpringerLink
Log in

Regression and artificial neural network models for strength properties of engineered cementitious composites

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This paper describes the development of regression and artificial neural network (ANN) models to determine the 28-day compressive and tensile strength of engineered cementitious composite (ECC) based on the mix design parameters. One hundred eighty ECC mixtures having variable mix designs were obtained from pervious experiments. Factors influencing the strengths were examined to determine the appropriate parameters for the ANN models. The optimized input parameters using training and development of ANN models were used to formulate the regression models. The ANN and regression models were tested with new sets of data for performance validation. Based on the good agreement and other statistical performance parameters, optimized ANN and regression models capable of predicting the strengths of ECC mixtures (using arbitrary mix design parameters) were developed and suggested for practical applications. ANN and regression models demonstrated excellent predictive ability showing predicted experimental strength ratio ranging between 0.95 and 1.00.

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

Similar content being viewed by others

References

  1. Li VC, Kanda T (1998) Engineered cementitious composites for structural applications. ASCE J Mater Civ Eng 10(2):66–69

    Article  Google Scholar 

  2. Wang S, Li VC (2006) High-early-strength engineered cementitious composites. ACI Mater J 103(2):97–105

    Google Scholar 

  3. Şahmaran M, Lachemi M, Hossain KMA, Ranade R, Li VC (2009) Influence of aggregate type and size on ductility and mechanical properties of engineered cementitious composites. ACI Mater J 106(3):308–316

    Google Scholar 

  4. Li VC (2003) On engineered cementitious composites (ECC)—a review of the material and its applications. J Adv Concr Technol 1(3):215–230

    Article  Google Scholar 

  5. Li VC, Wu C, Wang S, Ogawa A, Saito T (2002) Interface tailoring for strain-hardening PVA-ECC. ACI Mater J 99(5):463–472

    Google Scholar 

  6. Pan J, Yuan F, Luo M, Leung K (2012) Effect of composition on flexural behavior of engineered cementitious composites. Sci China Technol Sci 55(12):3425–3433

    Article  Google Scholar 

  7. Kan L, Shi H (2012) Investigation of self-healing behavior of engineered cementitious composites (ECC) materials. Constr Build Mater 29:348–356

    Article  Google Scholar 

  8. Sherir MAA, Hossain KMA, Lachemi M (2015) Structural performance of polymer fiber reinforced engineered cementitious composites subjected to static and fatigue flexural loading. Polymers 7:1299–1330

    Article  Google Scholar 

  9. Huang X, Ranade R, Ni W, Li VC (2013) Development of green engineered cementitious composites using iron ore tailings as aggregates. Constr Build Mater 44(3):757–764

    Article  Google Scholar 

  10. Mavani MB (2012) Fresh/mechanical/durability properties and structural performance of engineered cementitious composite (ECC). MASc Thesis, Ryerson University, Toronto, Canada

  11. Li M, Li VC (2011) High-early-strength engineered cementitious composites for fast, durable concrete repair—material properties. ACI Mater J 108(1):3–12

    Google Scholar 

  12. Kong H, Bike S, Li VC (2003) Constitutive rheological control to develop a self-consolidating engineered cementitious composite reinforced with hydrophilic poly(vinylalcohol) fibers. J Cem Concr Compos 25(3):333–341

    Article  Google Scholar 

  13. Oreta A, Kawashima K (2003) Neural Network Modeling of confined compressive strength and strain of circular concrete columns. J Struct Eng 129(4):554–561

    Article  Google Scholar 

  14. Sadrmomtazi A, Sobhani J, Mirgozar MA (2013) Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS. Constr Build Mater 42:205–216

    Article  Google Scholar 

  15. Tayfur G, Erdem T, Kırca Ö (2013) Strength prediction of high strength concrete by fuzzy logic and artificial neural networks. J Mater Civ Eng 10:1–33

    Google Scholar 

  16. Barbuta M, Diaconescu R, Harja M (2012) Using neural networks for prediction of properties of polymer concrete with fly ash. J Mater Civ Eng 24(5):523–528

    Article  Google Scholar 

  17. Gupta R, Kewalramani M, Goel A (2006) Prediction of concrete strength using neural-expert system. J Mater Civ Eng 18(3):462–466

    Article  Google Scholar 

  18. Baykasoğlu A, Dereli T, Tanış S (2004) Prediction of cement strength using soft computing techniques. Cem Concr Res 34(11):2083–2090

    Article  Google Scholar 

  19. Akkurt S, Ozdemira S, Tayfurb G, Akyolc B (2003) The use of GA–ANNs in the modelling of compressive strength of cement mortar. Cem Concr Res 33(7):973–979

    Article  Google Scholar 

  20. Motamedia S, Shamshirband S, Hashima R, Petkovićd D, Roya C (2015) Estimating unconfined compressive strength of cockle shell–cement–sand mixtures using soft computing methodologies. Eng Struct 98:49–58

    Article  Google Scholar 

  21. Motamedia S, Shamshirband S, Petkovićd D, Hashim R (2015) Application of adaptive neuro-fuzzy technique to predict the unconfined compressive strength of PFA-sand-cement mixture. Powder Technol 278:278–285

    Article  Google Scholar 

  22. Ozgan E (2011) Artificial neural network based modelling of the Marshall Stability of asphalt concrete. Expert Syst Appl 38(5):6025–6030

    Article  Google Scholar 

  23. Ozgan E (2009) Fuzzy logic and statistical-based modelling of the Marshall stability of asphalt concrete under varying temperatures and exposure times. Adv Eng Softw 40(7):527–534

    Article  MATH  Google Scholar 

  24. Kshirsagar A, Rathod M (2012) Artificial neural network. IJCA Proc Natl Conf Recent Trends Comput 2:12–16

    Google Scholar 

  25. Hossain KMA, Lachemi M, Easa SM (2006) Artificial neural network model for the strength prediction of fully restrained RC slabs subjected to membrane action. Comput Concr 3(6):1–16

    Article  Google Scholar 

  26. Willmott CJ (1982) Some comments on the evaluation of model performance. Bull Am Meteorol Soc 63(11):1309–1313

    Article  Google Scholar 

  27. Alilou VK, Teshnehlab M (2010) Prediction of 28-day compressive strength of concrete on the third day using artificial neural networks. IJE 3(6):565–576

    Google Scholar 

  28. Kan L, Shi H, Sakulich AR, Li VC (2010) Self-Healing characterization of engineered cementitious composite materials. ACI Mater J 107(6):619–626

    Google Scholar 

  29. Kim YY, Kong H, Li VC (2003) Design of engineered cementitious composite suitable for wet-mixture shotcreting. ACI Mater J 100(6):511–518

    Google Scholar 

  30. Lepech MD, Li VC (2008) Large-Scale processing of engineered cementitious composites. ACI Mater J 105(4):358–366

    Google Scholar 

  31. Lepech M, Keoleian GA, Qian S, Li VC (2008) Design of green engineered cementitious composites for pavement overlay applications, life-cycle civil engineering. Taylor & Francis Group, London, pp 837–842

    Google Scholar 

  32. Li VC, Yang E, Li M (2008) Field demonstration of durable link slabs for jointless bridge decks based on strain-hardening Engineered cementitious composites—Phase 3: shrinkage control. Michigan Department of Transportation, pp 1–110

  33. Ozbay E, Karahan O, Lachemi M, Hossain KMA, Atis CD (2012) Investigation of properties of engineered cementitious composites incorporating high volumes of fly ash and metakaolin. ACI Mater J 109(5):565–572

    Google Scholar 

  34. Sahmaran M, Yücel HE, Demirhan S, Arık MT, Li VC (2012) Combined effect of aggregate and mineral admixtures on tensile ductility of engineered cementitious composites. ACI Mater J 109(6):627–638

    Google Scholar 

  35. Sahmaran M, Lachemi M, Li VC (2010) Assessing mechanical properties and microstructure of fire-damaged engineered cementitious composites. ACI Mater J 107(3):297–304

    Google Scholar 

  36. Wang S, Li VC (2007) Engineered cementitious composites with high-volume fly ash. ACI Mater J 104(3):233–241

    Google Scholar 

  37. Wang S, Li VC (2003) Lightweight engineered cementitious composites (ECC). In: HPFRCC-4- International RILEM Workshop. RILEM, Ann Arbor pp 379–390

  38. Yang EH, Sahmaran M, YINGZI Y, Li VC (2009) Rheological control in production of engineered cementitious composites. ACI Mater J 106(4):357–366

    Google Scholar 

  39. Yang E, Yang Y, Li VC (2007) Use of high volumes of fly ash to improve ECC mechanical properties and material greenness. ACI Mater J 104(6):620–628

    Google Scholar 

  40. Chu K, Hossain KMA (2013) Modeling axial strength behaviour of concrete filled steel tube columns using Artificial Neural Network. In: CSCE General Conference, Montréal, Québec

  41. Yao X (1999) Evolving artificial neural networks. IEEE 87(9):1423–1447

    Article  Google Scholar 

  42. Mukherjee I, Routroy S (2012) Comparing the performance of neural networks developed by using Levenberg–Marquardt and Quasi–Newton with the gradient descent algorithm for modelling a multiple response grinding process. Expert Syst Appl 39(3):2397–2407

    Article  Google Scholar 

  43. Huang X, Ranade R, Zhang Q, Ni W, Li VC (2013) Mechanical and thermal properties of green lightweight engineered cementitious composites. Constr Build Mater 48:954–960

    Article  Google Scholar 

Download references

Acknowledgments

Authors acknowledge the financial support from Natural Science and Engineering Research Council (NSERC), Canada, for this project.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Ryerson University, Toronto, ON, Canada

    Khandaker M. A. Hossain & Shirin G. Samani

  2. Department of Engineering Science, University of Toronto, Toronto, ON, Canada

    Muhammed S. Anwar

Authors
  1. Khandaker M. A. Hossain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhammed S. Anwar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shirin G. Samani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Khandaker M. A. Hossain.

Ethics declarations

Conflict of interest

There is no potential conflict of interest.

Human and animal rights statement

Research does not involve human participants and/or animals, and consent to submit has been received explicitly from all co-authors, as well as from the responsible authorities (tacitly or explicitly) at the institute/organization where the work has been carried out.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hossain, K.M.A., Anwar, M.S. & Samani, S.G. Regression and artificial neural network models for strength properties of engineered cementitious composites. Neural Comput & Applic 29, 631–645 (2018). https://doi.org/10.1007/s00521-016-2602-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2602-3

Keywords

Search

Navigation