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.
A Bayesian Network for the Definition of Probability Models for Compressive Strength of Concrete Homogeneous Population
CROCE, PIETRO;Landi, Filippo
2017-01-01
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.File | Dimensione | Formato | |
---|---|---|---|
14thIPW_Bayesian_network_concrete_strength.pdf
solo utenti autorizzati
Descrizione: Articolo completo
Tipologia:
Versione finale editoriale
Licenza:
NON PUBBLICO - Accesso privato/ristretto
Dimensione
237.23 kB
Formato
Adobe PDF
|
237.23 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.