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<title>Non-destructive density-corrected estimation of the elastic modulus of slag-cement self-compacting concrete containing recycled aggregate</title>
<creator>Revilla Cuesta, Víctor</creator>
<creator>Shi, Jin-yan</creator>
<creator>Skaf Revenga, Marta</creator>
<creator>Ortega López, Vanesa</creator>
<creator>Manso Villalaín, Juan Manuel</creator>
<subject>Self-compacting concrete</subject>
<subject>Recycled aggregate</subject>
<subject>Ground granulated blast-furnace slag</subject>
<subject>Aggregate powder</subject>
<subject>Modulus of elasticity</subject>
<subject>Non-destructive testing</subject>
<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.</description>
<date>2023-11-13</date>
<date>2023-11-13</date>
<date>2022-12</date>
<type>info:eu-repo/semantics/article</type>
<identifier>2666-1659</identifier>
<identifier>http://hdl.handle.net/10259/7996</identifier>
<identifier>10.1016/j.dibe.2022.100097</identifier>
<language>eng</language>
<relation>Developments in the Built Environment. 2022, V. 12, 100097</relation>
<relation>https://doi.org/10.1016/j.dibe.2022.100097</relation>
<rights>http://creativecommons.org/licenses/by-nc-nd/4.0/</rights>
<rights>info:eu-repo/semantics/openAccess</rights>
<rights>Attribution-NonCommercial-NoDerivatives 4.0 Internacional</rights>
<publisher>Elsevier</publisher>
</thesis></metadata></record></GetRecord></OAI-PMH>