Bayesian neural networks and Gaussian processes in identification of concrete properties

  • Marek Słoński Institute for Computational Civil Engineering, Cracow University of Technology

Abstract

This paper gives a concise overview of concrete properties prediction using advanced nonlinear regression approach and Bayesian inference. Feed-forward layered neural network (FLNN) with Markov chain Monte Carlo stochastic sampling and Gaussian process (GP) with maximum likelihood hyperparameters estimation are introduced and compared. An empirical assessment of these two models using two benchmark problems are presented. Results on these benchmark datasets show that Bayesian neural networks and Gaussian processes have rather similar prediction accuracy and are superior in comparison to linear regression model.

Keywords

nonlinear regression, Bayesian methods, concrete, neural network, Gaussian process,

References

Published
Jan 25, 2017
How to Cite
SŁOŃSKI, Marek. Bayesian neural networks and Gaussian processes in identification of concrete properties. Computer Assisted Methods in Engineering and Science, [S.l.], v. 18, n. 4, p. 291–302, jan. 2017. ISSN 2299-3649. Available at: <https://cames.ippt.pan.pl/index.php/cames/article/view/108>. Date accessed: 25 may 2022.
Section
Articles