HPC strength prediction using Bayesian neural networks
Keywords:
Bayesian inference, regression, high-performance concrete, neural networkAbstract
The objective of this paper is to investigate the efficiency of nonlinear Bayesian regression for modelling and predicting strength properties of high-performance concrete (HPC). A multilayer perceptron neural network (MLP) model is used. Two statistical approaches to learning and prediction for MLP based on the likelihood function maximization and Bayesian inference are applied and compared. Results of experimental data sets show that Bayesian approach for MLP offers some advantages over classical one.
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. See http) /www.mpia-hd.mpg.de/homes/calj/
[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] F. de Larrard. Concrete Mixture Proportioning - A scientific approach. E&FN SPON, London, 1999.
[5] S.W. Forster. High-performance concrete-stretching the paradigm. Concrete International, 16(10): 33-34, Oct. 1994.
[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] F. de Larrard. Concrete Mixture Proportioning - A scientific approach. E&FN SPON, London, 1999.
[5] S.W. Forster. High-performance concrete-stretching the paradigm. Concrete International, 16(10): 33-34, Oct. 1994.
Published
2022-08-24
Issue
pp. 345-352
Section
Articles
License
Copyright © The Author(s).
This work is licensed under the Creative Commons Attribution 4.0 International CC BY 4.0.
How to Cite
Słoński, M. (2022). HPC strength prediction using Bayesian neural networks. Computer Assisted Methods in Engineering and Science, 14(2), 345-352. https://cames3.ippt.pan.pl/index.php/cames/article/view/836
