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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ang H-S, Tang WH (2007) Probability concepts in engineering: emphasis on applications to civil and environmental engineering, vol. 1, 2nd edn. Hoboken, NJ, Wiley
Bartlett FM, MacGregor JG (1994) Effect of core diameter on concrete core strengths. ACI Mater J
Berthold MR, Borgelt C, Höppner F, Klawonn F (2010) Guide to intelligent data analysis. Springer
Breysse D (2012) Non-destructive assessment of concrete structures: reliability and limits of single and combined techniques. State of the art report of the RILEM technical committee 207 INR. Springer
Cianfrone F, Facaoaru I (1979) Study on the introduction into Italy of the combined non-destructive method for the determination of in situ concrete strength
Deublein M, Schlosser M, Faber MH (2011) Hierarchical modeling of structural timber material properties by means of bayesian probabilistic networks
D.L. 1939 16/11/1939, Norme per l’accettazione dei Leganti idraulici, Rome, Italy (In Italian), 1939
D.M. 1972 L.L. 30/5/1972. Norme tecniche alle quali devono uniformarsi le costruzioni in conglomerato cementizio normale e precompresso e a struttura metallica, Rome, Italy (In Italian), 1972
Fiore A, Porco F, Uva G, Mezzina M (2013) On the dispersion of data collected in situ diagnostic of the existing concrete. Constr Build Mater 47
Giannini R, Sguerri L, Paolacci F, Alessandri S (2014) Assessment of concrete strength combining direct and NDT measures via Bayesian inference. Eng Struct 64
Jensen FV, Nielsen TD (2007) Bayesian networks and decision graphs. Springer
Kjærulff UB, Madsen AL (2008) Bayesian networks and influence diagrams. Springer
Kruse R, Borgelt C, Klawonn F, Moewes C, Steinbrecher M, Held P (2013) Computational intelligence: a methodological introduction. Springer
MacLachlan G, Peel D (2000) Finite mixture models. Wiley
Neville AM (2004) Properties of concrete. Pearson education limited
Pereira N, Romão X (2016) Assessment of the concrete strength in existing buildings using a finite population approach. Constr Build Mater 110
Santarella L (1969) Il Cemento Armato. la Tecnica e la Statica, Hoepli
Sousa HS, Machado JS, Branco JM, Lourenço PB (2015) Onsite assessment of structural timber by means of hierarchical models and probabilistic methods. Constr Build Mater
Verderame GM, Manfredi G (2001) Le proprietà meccaniche dei calcestruzzi impiegati nelle strutture in cemento armato realizzate negli anni ’60, X Congresso Nazionale “L’ingegneria Sismica in Italia”, (In Italian)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Marsili, F., Croce, P., Klawonn, F., Landi, F. (2017). A Bayesian Network for the Definition of Probability Models for Compressive Strength of Concrete Homogeneous Population. In: Caspeele, R., Taerwe, L., Proske, D. (eds) 14th International Probabilistic Workshop . Springer, Cham. https://doi.org/10.1007/978-3-319-47886-9_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-47886-9_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47885-2
Online ISBN: 978-3-319-47886-9
eBook Packages: EngineeringEngineering (R0)