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Probabilistic approach to concrete durability based on combined stochastic differential equations and Monte Carlo method

  • Cementitious materials are often exposed to aggressive environments, which have a significant impact on their durability. Proper prediction of concrete corrosion helps to apply the right measures and technologies, to extend the service life of structures. Carbonation and cyclic freezing are recognized among the most common corrosive factors for concrete. Their progress is linked to the penetration of CO2 and water into the concrete structure. Due to the random arrangement of aggregates and cement paste, concrete is an inhomogeneous material. Therefore, the progress of carbonation and frost-induced damage should be treated as random variables with appropriate probabilistic parameters. Experimental studies on concrete carbonation and freezing were conducted in accordance with the standards EN 12390–12 and EN 12390–9. As observed in the experiments, the progress of carbonation and frost damage of concrete could be described by zigzag, not necessarily monotonic functions. StochasticCementitious materials are often exposed to aggressive environments, which have a significant impact on their durability. Proper prediction of concrete corrosion helps to apply the right measures and technologies, to extend the service life of structures. Carbonation and cyclic freezing are recognized among the most common corrosive factors for concrete. Their progress is linked to the penetration of CO2 and water into the concrete structure. Due to the random arrangement of aggregates and cement paste, concrete is an inhomogeneous material. Therefore, the progress of carbonation and frost-induced damage should be treated as random variables with appropriate probabilistic parameters. Experimental studies on concrete carbonation and freezing were conducted in accordance with the standards EN 12390–12 and EN 12390–9. As observed in the experiments, the progress of carbonation and frost damage of concrete could be described by zigzag, not necessarily monotonic functions. Stochastic differential equations (SDE) were employed to predict the behavior of concrete exposed to elevated CO2 concentrations and cyclic freezing. The stochastic model consisted of a drift term, which described the general trend of concrete durability exposed to carbonation and frost cycles, as well as a diffusion term, which accounted for the stochastic features of inhomogeneous concrete microstructure. The Euler–Maruyama approximation with Milstein improvement was applied to model the realization of the stochastic changes in concrete microstructure/durability. The proposed approach predicted experimental results with high accuracy. The application of the Monte Carlo (MC) method with 100,000 SDE realizations allowed to calculate the statistical parameters of the processes, such as concrete carbonation and freezing cycles. The probabilistic parameters, such as expected values and standard deviations, calculated using the SDE_MC approach, were in good agreement with experimental results for both problems, i.e. decelerating concrete carbonation and accelerating concrete scaling.zeige mehrzeige weniger

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Metadaten
Autor*innen:M. Koniorczyk, Dalia Bednarska, A. Wieczorek, K. Materak
Dokumenttyp:Zeitschriftenartikel
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Archives of Civil and Mechanical Engineering
Jahr der Erstveröffentlichung:2025
Organisationseinheit der BAM:7 Bauwerkssicherheit
7 Bauwerkssicherheit / 7.1 Baustoffe
Veröffentlichende Institution:Bundesanstalt für Materialforschung und -prüfung (BAM)
Verlag:Springer Nature Switzerland AG
Verlagsort:Schweiz
Jahrgang/Band:25
Ausgabe/Heft:5-6
Aufsatznummer:287
Erste Seite:1
Letzte Seite:31
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurbau
Freie Schlagwörter:Concrete carbonation; Concrete durability; Concrete scaling; Stochastic differential equations; Time-dependent random variable
Themenfelder/Aktivitätsfelder der BAM:Infrastruktur
Infrastruktur / Green Intelligent Building
DOI:10.1007/s43452-025-01335-y
URN:urn:nbn:de:kobv:b43-643417
ISSN:2083-3318
Verfügbarkeit des Dokuments:Datei für die Öffentlichkeit verfügbar ("Open Access")
Lizenz (Deutsch):License LogoCreative Commons - CC BY - Namensnennung 4.0 International
Datum der Freischaltung:13.10.2025
Referierte Publikation:Ja
Datum der Eintragung als referierte Publikation:13.10.2025
Schriftenreihen ohne Nummerierung:Wissenschaftliche Artikel der BAM
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