Bayesian regression approaches on example of concrete fatigue failure prediction

Authors

  • Marek Słoński Cracow University of Technology
    Poland

Keywords:

Bayesian inference, concrete, fatigue failure, Gaussian process regression, feed-forward neural Network

Abstract

The focus of this paper is the application of two nonlinear regression models in the context of Bayesian inference to the problem of failure prediction of concrete specimen under repeated loads based on experimental data. These two models are compared with an empirical formulae. Results on testing data show that both models give better point predictions than empirical formulae. Moreover, Bayesian regression approach makes it possible to calculate prediction intervals (error bars) describing the reliability of the models predictions.

References

[1] C.A.L. Bailer-Jones, T.J. Sabin, D.J.C. MacKay, 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.
[2] C.M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, Oxford, 1995.
[3] C.M. Bishop. Pattern Recognition and Machine Learning. Springer, New York, 2006.
[4] C.M. Bishop, M.E. Tipping. Bayesian regression and classification. In: J. Suykens, G. Horvath, S. Basu, C. Micchelli, J. Vandewalle, eds., Advances in Learning Theory: Methods, Models and Applications, NATO Science Series III: Computer and Systems Sciences, 267- 285. IOS Press, 2003.
[5] K. Furtak. Strength of the concrete under multiple repeated loads, (in Polish). Arch. Civil Engrg., 30, 1984.

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Published

2022-09-27

Issue

pp. 655- 668

Section

Articles

How to Cite

Słoński, M. (2022). Bayesian regression approaches on example of concrete fatigue failure prediction. Computer Assisted Methods in Engineering and Science, 13(4), 655-668. https://cames3.ippt.pan.pl/index.php/cames/article/view/896

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