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<dc:title>Non-destructive density-corrected estimation of the elastic modulus of slag-cement self-compacting concrete containing recycled aggregate</dc:title>
<dc:creator>Revilla Cuesta, Víctor</dc:creator>
<dc:creator>Shi, Jin-yan</dc:creator>
<dc:creator>Skaf Revenga, Marta</dc:creator>
<dc:creator>Ortega López, Vanesa</dc:creator>
<dc:creator>Manso Villalaín, Juan Manuel</dc:creator>
<dc:subject>Self-compacting concrete</dc:subject>
<dc:subject>Recycled aggregate</dc:subject>
<dc:subject>Ground granulated blast-furnace slag</dc:subject>
<dc:subject>Aggregate powder</dc:subject>
<dc:subject>Modulus of elasticity</dc:subject>
<dc:subject>Non-destructive testing</dc:subject>
<dc:subject>Ingeniería civil</dc:subject>
<dc:subject>Materiales de construcción</dc:subject>
<dc:subject>Civil engineering</dc:subject>
<dc:subject>Building materials</dc:subject>
<dc:description>Non-destructive tests that cause no damage to concrete components can be used in rehabilitation works to&#xd;
determine concrete mechanical properties, such as the modulus of elasticity. In this paper, models are presented&#xd;
to predict the modulus of elasticity of Self-Compacting Concrete (SCC) with Recycled Aggregate (RA) and slag&#xd;
cement through the hammer rebound index and ultrasonic pulse velocity. Simple- and multiple-regression&#xd;
models were developed to estimate the modulus of elasticity according to the monotonic relation between&#xd;
variables shown by Spearman correlations. In these models, the modulus of elasticity was always inversely&#xd;
proportional to the square root of the non-destructive property under consideration, and the hardened density&#xd;
raised to the power of 2.5 as correction factor always increased estimation accuracy and robustness. The&#xd;
multiple-regression model with density correction yielded the most precise estimations of the elastic modulus&#xd;
with deviations below ±10% and ±20% in 82% and 94% of cases, respectively.</dc:description>
<dc:description>This research work was supported by the Spanish Ministry of Universities, MICINN, AEI, EU, ERDF and NextGenerationEU/PRTR [grant numbers PID2020-113837RB-I00; 10.13039/501100011033; TED2021-129715B–I00; FPU17/03374]; the Junta de Castilla y León (Regional Government) and ERDF [grant number UIC-231]; and, finally, the University of Burgos [grant number SUCONS, Y135.GI].</dc:description>
<dc:date>2023-11-13T08:05:40Z</dc:date>
<dc:date>2023-11-13T08:05:40Z</dc:date>
<dc:date>2022-12</dc:date>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
<dc:identifier>2666-1659</dc:identifier>
<dc:identifier>http://hdl.handle.net/10259/7996</dc:identifier>
<dc:identifier>10.1016/j.dibe.2022.100097</dc:identifier>
<dc:language>eng</dc:language>
<dc:relation>Developments in the Built Environment. 2022, V. 12, 100097</dc:relation>
<dc:relation>https://doi.org/10.1016/j.dibe.2022.100097</dc:relation>
<dc:rights>Attribution-NonCommercial-NoDerivatives 4.0 Internacional</dc:rights>
<dc:rights>http://creativecommons.org/licenses/by-nc-nd/4.0/</dc:rights>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:publisher>Elsevier</dc:publisher>
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