Prediction of concrete fatigue durability using Bayesian neural networks

Authors

  • Marek Słoński Cracow University of Technology
    Poland

Keywords:

Bayesian neural networks, concrete fatigue durability, prediction

Abstract

The utility of Bayesian neural networks to predict concrete fatigue durability as a function of concrete mechanical parameters of a specimen and characteristics of the loading cycle is investigated. Bayesian approach to learning neural networks allows automatic control of the complexity of the non-linear model, calculation of error bars and automatic determination of the relevance of various input variables. Comparative results on experimental data set show that Bayesian neural network works well.

References

[1] C.A.L. Bailer-Jones, T.J. Sabin, D.J.C. MacKay, and P.J. Withers. Prediction of deformed and annealed microstructures using Bayesian neural networks and Gaussian processes. In: Proc. of the Australia-Pacific Forum on Intelligent Processing and Manufacturing of Materials. 1997. See http://www.mpia-hd.mpg.de/homes/ calj/.
[2] C.M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, Oxford, 1995. See http://research.microsoft.com/~cmbishop/.
[3] K. Furtak. Strength of the concrete under multiple repeated loads (in Polish). Arch. of Civil Eng., 30, 1984.
[4] D. Husmeier. Neural Networks for Conditional Probability Estimation: Forecasting Beyond Point Predictions. Perspectives in Neural Computing. Springer London, 1999.
[5] J. Kaliszuk, A. Urbańska, Z. Waszczyszyn, and K. Furtak. Neural analysis of concrete fatigue durability on the basis of experimental evidence. Arch. of Civil Eng., 38, 2001.

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Published

2022-11-30

Issue

pp. 259-265

Section

Articles

How to Cite

Słoński, M. (2022). Prediction of concrete fatigue durability using Bayesian neural networks. Computer Assisted Methods in Engineering and Science, 12(2-3), 259-265. https://cames3.ippt.pan.pl/index.php/cames/article/view/994

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