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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/7996

    Título
    Non-destructive density-corrected estimation of the elastic modulus of slag-cement self-compacting concrete containing recycled aggregate
    Autor
    Revilla Cuesta, VíctorUBU authority Orcid
    Shi, Jin-yan
    Skaf Revenga, MartaUBU authority Orcid
    Ortega López, VanesaUBU authority Orcid
    Manso Villalaín, Juan ManuelUBU authority Orcid
    Publicado en
    Developments in the Built Environment. 2022, V. 12, 100097
    Editorial
    Elsevier
    Fecha de publicación
    2022-12
    ISSN
    2666-1659
    DOI
    10.1016/j.dibe.2022.100097
    Abstract
    Non-destructive tests that cause no damage to concrete components can be used in rehabilitation works to determine concrete mechanical properties, such as the modulus of elasticity. In this paper, models are presented to predict the modulus of elasticity of Self-Compacting Concrete (SCC) with Recycled Aggregate (RA) and slag cement through the hammer rebound index and ultrasonic pulse velocity. Simple- and multiple-regression models were developed to estimate the modulus of elasticity according to the monotonic relation between variables shown by Spearman correlations. In these models, the modulus of elasticity was always inversely proportional to the square root of the non-destructive property under consideration, and the hardened density raised to the power of 2.5 as correction factor always increased estimation accuracy and robustness. The multiple-regression model with density correction yielded the most precise estimations of the elastic modulus with deviations below ±10% and ±20% in 82% and 94% of cases, respectively.
    Palabras clave
    Self-compacting concrete
    Recycled aggregate
    Ground granulated blast-furnace slag
    Aggregate powder
    Modulus of elasticity
    Non-destructive testing
    Materia
    Ingeniería civil
    Civil engineering
    Materiales de construcción
    Building materials
    URI
    http://hdl.handle.net/10259/7996
    Versión del editor
    https://doi.org/10.1016/j.dibe.2022.100097
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