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Optimizing Adaptable Systems for Future Uncertainty Read chapter Read first chapter

2017 | OriginalPaper | Chapter

A Bayesian Network for the Definition of Probability Models for Compressive Strength of Concrete Homogeneous Population

Authors : F. Marsili, P. Croce, F. Klawonn, F. Landi

Published in: 14th International Probabilistic Workshop

Publisher: Springer International Publishing

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Abstract

A methodology for the definition of probabilistic models for concrete compressive strength through the outcomes of non-destructive investigation is presented. Results of standard compressive tests on concrete are collected, and, in order to identify homogeneous concrete populations, corresponding to individual concrete classes, an innovative approach is suggested, based on fitting the crude histogram of all available data with a mixture model. The results lead to the definition of a Bayesian Network whose nodes are represented by the concrete class and the concrete compressive strength. The network is improved with a further variable representing the strength estimated through non-destructive tests. The concrete compressive strength will be thus inferred using the network, considering the estimated resistance.

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previous chapter A Bayesian Network for the Definition of Probability Models for Masonry Mechanical Parameters
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Metadata
Title
A Bayesian Network for the Definition of Probability Models for Compressive Strength of Concrete Homogeneous Population
Authors
F. Marsili
P. Croce
F. Klawonn
F. Landi
Copyright Year
2017
Book
14th International Probabilistic Workshop

Print ISBN: 978-3-319-47885-2

Electronic ISBN: 978-3-319-47886-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-47886-9_19