Neural Computing and Applications

, Volume 29, Issue 9, pp 631–645 | Cite as

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

  • Khandaker M. A. Hossain
  • Muhammed S. Anwar
  • Shirin G. Samani
Original Article
First Online:
Received:
Accepted:

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.

Keywords

Engineered cementitious composite Artificial neural network model Regression model Strength properties Training Validation 
This is a preview of subscription content, log in to check access

Notes

Acknowledgments

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

Compliance with ethical standards

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.

References

  1. 1.
    Li VC, Kanda T (1998) Engineered cementitious composites for structural applications. ASCE J Mater Civ Eng 10(2):66–69CrossRefGoogle Scholar
  2. 2.
    Wang S, Li VC (2006) High-early-strength engineered cementitious composites. ACI Mater J 103(2):97–105Google Scholar
  3. 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):308–316Google Scholar
  4. 4.
    Li VC (2003) On engineered cementitious composites (ECC)—a review of the material and its applications. J Adv Concr Technol 1(3):215–230CrossRefGoogle Scholar
  5. 5.
    Li VC, Wu C, Wang S, Ogawa A, Saito T (2002) Interface tailoring for strain-hardening PVA-ECC. ACI Mater J 99(5):463–472Google Scholar
  6. 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):3425–3433CrossRefGoogle Scholar
  7. 7.
    Kan L, Shi H (2012) Investigation of self-healing behavior of engineered cementitious composites (ECC) materials. Constr Build Mater 29:348–356CrossRefGoogle Scholar
  8. 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 7:1299–1330CrossRefGoogle Scholar
  9. 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):757–764CrossRefGoogle Scholar
  10. 10.
    Mavani MB (2012) Fresh/mechanical/durability properties and structural performance of engineered cementitious composite (ECC). MASc Thesis, Ryerson University, Toronto, CanadaGoogle Scholar
  11. 11.
    Li M, Li VC (2011) High-early-strength engineered cementitious composites for fast, durable concrete repair—material properties. ACI Mater J 108(1):3–12Google Scholar
  12. 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):333–341CrossRefGoogle Scholar
  13. 13.
    Oreta A, Kawashima K (2003) Neural Network Modeling of confined compressive strength and strain of circular concrete columns. J Struct Eng 129(4):554–561CrossRefGoogle Scholar
  14. 14.
    Sadrmomtazi A, Sobhani J, Mirgozar MA (2013) Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS. Constr Build Mater 42:205–216CrossRefGoogle Scholar
  15. 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 10:1–33Google Scholar
  16. 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):523–528CrossRefGoogle Scholar
  17. 17.
    Gupta R, Kewalramani M, Goel A (2006) Prediction of concrete strength using neural-expert system. J Mater Civ Eng 18(3):462–466CrossRefGoogle Scholar
  18. 18.
    Baykasoğlu A, Dereli T, Tanış S (2004) Prediction of cement strength using soft computing techniques. Cem Concr Res 34(11):2083–2090CrossRefGoogle Scholar
  19. 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):973–979CrossRefGoogle Scholar
  20. 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 98:49–58CrossRefGoogle Scholar
  21. 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 278:278–285CrossRefGoogle Scholar
  22. 22.
    Ozgan E (2011) Artificial neural network based modelling of the Marshall Stability of asphalt concrete. Expert Syst Appl 38(5):6025–6030CrossRefGoogle Scholar
  23. 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):527–534CrossRefMATHGoogle Scholar
  24. 24.
    Kshirsagar A, Rathod M (2012) Artificial neural network. IJCA Proc Natl Conf Recent Trends Comput 2:12–16Google Scholar
  25. 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):1–16CrossRefGoogle Scholar
  26. 26.
    Willmott CJ (1982) Some comments on the evaluation of model performance. Bull Am Meteorol Soc 63(11):1309–1313CrossRefGoogle Scholar
  27. 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):565–576Google Scholar
  28. 28.
    Kan L, Shi H, Sakulich AR, Li VC (2010) Self-Healing characterization of engineered cementitious composite materials. ACI Mater J 107(6):619–626Google Scholar
  29. 29.
    Kim YY, Kong H, Li VC (2003) Design of engineered cementitious composite suitable for wet-mixture shotcreting. ACI Mater J 100(6):511–518Google Scholar
  30. 30.
    Lepech MD, Li VC (2008) Large-Scale processing of engineered cementitious composites. ACI Mater J 105(4):358–366Google Scholar
  31. 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 837–842Google Scholar
  32. 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 1–110Google Scholar
  33. 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):565–572Google Scholar
  34. 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):627–638Google Scholar
  35. 35.
    Sahmaran M, Lachemi M, Li VC (2010) Assessing mechanical properties and microstructure of fire-damaged engineered cementitious composites. ACI Mater J 107(3):297–304Google Scholar
  36. 36.
    Wang S, Li VC (2007) Engineered cementitious composites with high-volume fly ash. ACI Mater J 104(3):233–241Google Scholar
  37. 37.
    Wang S, Li VC (2003) Lightweight engineered cementitious composites (ECC). In: HPFRCC-4- International RILEM Workshop. RILEM, Ann Arbor pp 379–390Google Scholar
  38. 38.
    Yang EH, Sahmaran M, YINGZI Y, Li VC (2009) Rheological control in production of engineered cementitious composites. ACI Mater J 106(4):357–366Google Scholar
  39. 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):620–628Google Scholar
  40. 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, QuébecGoogle Scholar
  41. 41.
    Yao X (1999) Evolving artificial neural networks. IEEE 87(9):1423–1447CrossRefGoogle Scholar
  42. 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):2397–2407CrossRefGoogle Scholar
  43. 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 48:954–960CrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • Khandaker M. A. Hossain
    • 1
  • Muhammed S. Anwar
    • 2
  • Shirin G. Samani
    • 1
  1. 1.Department of Civil EngineeringRyerson UniversityTorontoCanada
  2. 2.Department of Engineering ScienceUniversity of TorontoTorontoCanada

Personalised recommendations