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Report of RILEM TC 281-CCC: Insights into factors affecting the carbonation rate of concrete with SCMs revealed from data mining and machine learning approaches

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dc.contributor.author A. Vollpracht pt_BR
dc.contributor.author G.J.G. Gluth pt_BR
dc.contributor.author B. Rogiers pt_BR
dc.contributor.author Q.T. Phung pt_BR
dc.contributor.author I.D. Uwanuakwa pt_BR
dc.contributor.author Y. Villagran Zaccardi pt_BR
dc.contributor.author C. Thiel pt_BR
dc.contributor.author H. Vanoutrive pt_BR
dc.contributor.author J.M. Etcheverry pt_BR
dc.contributor.author E. Gruyaert pt_BR
dc.contributor.author S. Kamali-Bernard pt_BR
dc.contributor.author A. Kanellopoulos pt_BR
dc.contributor.author Z. Zhao pt_BR
dc.contributor.author Martins, I. M. pt_BR
dc.contributor.author S. Rathnarajan pt_BR
dc.contributor.author De Belie, N. pt_BR
dc.date.accessioned 2025-01-13T10:05:22Z pt_BR
dc.date.accessioned 2025-04-16T13:34:59Z
dc.date.available 2025-01-13T10:05:22Z pt_BR
dc.date.available 2025-04-16T13:34:59Z
dc.date.issued 2024-10-23 pt_BR
dc.identifier.citation 10.1617/s11527-024-02465-0 pt_BR
dc.identifier.uri http://dspace2.lnec.pt:8080/jspui/handle/123456789/1018123 pt_BR
dc.identifier.uri http://repositorio.lnec.pt:8080/jspui/handle/123456789/1018123
dc.description.abstract The RILEM TC 281–CCC ‘‘Carbonation of concrete with supplementary cementitious materials’’ conducted a study on the effects of supplementary cementitious materials (SCMs) on the carbonation rate of blended cement concretes and mortars. In this context, a comprehensive database has been established, consisting of 1044 concrete and mortar mixes with their associated carbonation depth data over time. The dataset comprises mix designs with a large variety of binders with up to 94% SCMs, collected from the literature as well as unpublished testing reports. The data includes chemical composition and physical properties of the raw materials, mix-designs, compressive strengths, curing and carbonation testing conditions. Natural carbonation was recorded for several years in many cases with both indoor and outdoor results. The database has been analysed to investigate the effects of binder composition and mix design, curing and preconditioning, and relative humidity on the carbonation rate. Furthermore, the accuracy of accelerated carbonation testing as well as possible correlations between compressive strength and carbonation resistance were evaluated. One approach to summerise the physical and chemical resistance in one parameter is the ratio of water content to content of carbonatable CaO (w/CaOreactive ratio). The analysis revealed that the w/CaOreactive ratio is a decisive factor for carbonation resistance, while curing and exposure conditions also influence carbonation. Under natural exposure conditions, the carbonation data exhibit significant variations. Nevertheless, probabilistic inference suggests that both accelerated and natural carbonation processes follow a square-root-of-time behavior, though accelerated and natural carbonation cannot be converted into each other without corrections. Additionally, a machine learning technique was employed to assess the influence of parameters governing the carbonation progress in concretes. pt_BR
dc.language.iso eng pt_BR
dc.publisher Springer pt_BR
dc.rights openAccess pt_BR
dc.subject Natural carbonation pt_BR
dc.subject Accelerated carbonation pt_BR
dc.subject SCM pt_BR
dc.subject Database pt_BR
dc.title Report of RILEM TC 281-CCC: Insights into factors affecting the carbonation rate of concrete with SCMs revealed from data mining and machine learning approaches pt_BR
dc.type article pt_BR
dc.description.volume 57, 206 pt_BR
dc.description.sector DM/NMC pt_BR
dc.identifier.proc 0202/1303/21960 pt_BR
dc.description.magazine Materials and Structures pt_BR
dc.contributor.peer-reviewed SIM pt_BR
dc.contributor.academicresearchers SIM pt_BR
dc.contributor.arquivo NAO pt_BR


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