A methodology for the definition of probabilistic models for material strength through a visual analysis of masonry structures is presented. A Bayesian Network whose nodes are represented by the masonry class and masonry features is developed based on the so called Masonry Quality Index method. The network is improved taking into account further quantitative information derived from previously tested masonry structures, similar to the one under assessment, supplemented by engineering judgment on the masonry features. Masonry mechanical properties can be so inferred using the network, given the results of qualitative investigation. The so established probability model can be used for preliminary reliability analysis or as prior distribution for further updating.
A Bayesian Network for the Definitionof Probability Models for Masonry Mechanical Parameters / Marsili, F.; Croce, P.; Klawonn, F.; Vignoli, A.; Boschi, S.; Landi, F.. - ELETTRONICO. - (2017), pp. 253-268. (Intervento presentato al convegno 14th International Probabilistic Workshop).
A Bayesian Network for the Definitionof Probability Models for Masonry Mechanical Parameters
MARSILI, FRANCESCA;VIGNOLI, ANDREA;BOSCHI, SONIA;LANDI, FILIPPO
2017
Abstract
A methodology for the definition of probabilistic models for material strength through a visual analysis of masonry structures is presented. A Bayesian Network whose nodes are represented by the masonry class and masonry features is developed based on the so called Masonry Quality Index method. The network is improved taking into account further quantitative information derived from previously tested masonry structures, similar to the one under assessment, supplemented by engineering judgment on the masonry features. Masonry mechanical properties can be so inferred using the network, given the results of qualitative investigation. The so established probability model can be used for preliminary reliability analysis or as prior distribution for further updating.File | Dimensione | Formato | |
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IPW2016_A Bayesian Network for the Definition of Probability Models.pdf
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