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Building strength models for high-performance concrete at different ages using genetic operation trees, nonlinear regression, and neural networks

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Abstract

Because the behavior of HPC at early age may be rather different at late age, this study proposed to establish the strength models of HPC at different ages, and to explore the difference between these models. A large number of experimental data were used to compare accuracy of the three model building techniques, nonlinear regression analysis (NLRA), back-propagation networks (BPN), and genetic operation trees (GOT). The results showed: (1) when NLRA was employed to establish the prediction model, the approach to establish HPC strength models based on the three separate data sets was more accurate than that used to establish HPC strength models for the total data set. (2) If an explicit formula is necessary, GOT is the best choice to build concrete strength models at medium and late ages (i.e., more than 14 days), while NLRA provides greater accuracy at early ages (i.e., less than 14 days); otherwise, BPN is the best choice.

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References

  1. Haykin S (2005) Neural networks: a comprehensive foundation. Prentice Hall PTR, New Jersey

    Google Scholar 

  2. Welstead ST (1994) Neural network and fuzzy logic applications in C/C++. Wiley, New York

    Google Scholar 

  3. Yeh I-C (1999) Design of high performance concrete mixture using neural networks and nonlinear programming. J Comput Civ Eng 13(1):36–42

    Article  Google Scholar 

  4. Yeh I-C (1998) Modeling concrete strength with augment-neuron networks. J Mater Civ Eng 10(4):263–268

    Article  Google Scholar 

  5. Yeh I-C (1998) Modeling of strength of high performance concrete using artificial neural networks. Cem Concr Res 28(2):1797–1808

    Article  Google Scholar 

  6. Kim JI, Kim DK, Feng MQ, Yazdani F (2004) Application of neural networks for estimation of concrete strength. J Civ Eng 16(4):257–264

    Article  Google Scholar 

  7. Öztaş A, Pala M, Özbay E, Kanca E, Çagˇlar N, Bhatti MA (2006) Predicting the compressive strength and slump of high strength concrete using neural network. Constr Building Mater 20(9):769–775

    Article  Google Scholar 

  8. Haj-Ali RM, Kurtis KE, Akshay R (2001) Neural network modeling of concrete expansion during long-term sulfate exposure. ACI Mater J 98(1):36–43

    Google Scholar 

  9. Nehdi M, Djebbar Y, Khan A (2001) Neural network model for preformed-foam cellular concrete. ACI Mater J 98(5):402–409

    Google Scholar 

  10. Lien LC, Yeh IC, Cheng MY (2006) Modeling strength of high-performance concrete using genetic algorithms and operation tree. J Technol 21(1):41–54

    Google Scholar 

  11. Chen L (2003) A study of applying macro evolutionary genetic programming to concrete strength estimation. ASCE J Comput Civ Eng 17(4):290–294

    Article  Google Scholar 

  12. Ozbay E, Gesoglu M, Güneyisi E (2008) Empirical modeling of fresh and hardened properties of self-compacting concretes by genetic programming. Constr Building Mater 22(8):1831–1840

    Article  Google Scholar 

  13. Davis L (1991) Handbook of genetic algorithms. Van Nostrand Reinhold, New York

    Google Scholar 

  14. Goldberg DE (1989) Genetic algorithms in search optimization and machine learning. Addison–Wesley, Massachusetts

    MATH  Google Scholar 

  15. Larrard F (1993) Optimization of high-performance concrete. Seminar on high performance concrete, Taipei, Taiwan, 31–44

  16. Larrard F, Sedran T (1994) Optimization of ultra-high-performance concrete by the use of a packing model. Cem Concr Res 24(6):997–1009

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Science Council, ROC, under Grant NSC-96-2221-E-216-032.

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Authors and Affiliations

  1. Chung Hua University, Hsinchu city, Taiwan, ROC

    Chien-Hua Peng & I-Cheng Yeh

  2. National Taiwan University of Science and Technology, Taipei, Taiwan, ROC

    Li-Chuan Lien

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  1. Chien-Hua Peng
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  2. I-Cheng Yeh
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  3. Li-Chuan Lien
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Correspondence to I-Cheng Yeh.

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Peng, CH., Yeh, IC. & Lien, LC. Building strength models for high-performance concrete at different ages using genetic operation trees, nonlinear regression, and neural networks. Engineering with Computers 26, 61–73 (2010). https://doi.org/10.1007/s00366-009-0142-5

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  • DOI: https://doi.org/10.1007/s00366-009-0142-5

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