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Crack width prediction of RC structures by Artificial Neural Networks

  • Carlos Avila
  • Yukikazu Tsuji
  • Yoichi Shiraishi
Conference paper

Abstract

This paper proposes the use of Artificial Neural Networks (ANN) for the prediction of the maximum surface crack width of precast reinforced concrete beams joined by steel coupler connectors and anchor bars (jointed beams). Two different training algorithms are used in this study and their performances are compared. The first approach used Back propagation (BPANN) and the second one includes Genetic Algorithms (GANN) during the training process. Input and output vectors are designed on the basis of empirical equations available in the literature to estimate crack widths in common reinforced concrete (RC) structures and parameters that characterize the mechanical behavior of RC beams with overlapped reinforcement. Two well-defined points of loading are considered in this study to demonstrate the suitability of this approach in both, a linear and a highly nonlinear stage of the mechanical response of this type of structures. Remarkable results were obtained, however, in all cases the combined Genetic Artificial Neural Network approach resulted in improved prediction performance over networks trained by error back propagation.

Keywords

Artificial Neural Network Artificial Neural Network Model Reinforce Concrete Crack Width Reinforce Concrete Beam 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Avila, C., Tsuji, Y., Morita, T., and Iizuka, Y. (2003) Flexural behavior of precast RC beams joined by cast iron couplers. Cement Science and Concrete Technology, No.57: 683–690Google Scholar
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    Montana, D., Davis, L. (1989) Training Feed forward Neural Networks using Genetic Algorithms. Proceedings of the International Joint Conference on Artificial Intelligence: 762–767.Google Scholar
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    Shahin, M., Maier H. and Jaksa, M. (2000) Evolutionary data division methods for developing artificial neural network models in geotechnical engineering, Department of Civil & Environmental Engineering. The University of Adelaide. Research report No. R.171.Google Scholar
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    JSCE (2002) Standard Specifications for Concrete Structures-Structural Performance Verification JSCE, Japan (in Japanese).Google Scholar
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    http://www.compapp.dcu.ie. The backpropagation learning algorithm, School of Computer Applications, Dublin City UniversityGoogle Scholar

Copyright information

© Springer-Verlag/Wien 2005

Authors and Affiliations

  • Carlos Avila
    • 1
  • Yukikazu Tsuji
    • 1
  • Yoichi Shiraishi
    • 2
  1. 1.Department of Civil EngineeringGunma UniversityJapan
  2. 2.Department. of Computer SciencesGunma UniversityJapan

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