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.
Keywords:
Bayesian inference, concrete, fatigue failure, Gaussian process regression, feed-forward neural NetworkReferences
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[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.
