Title:
Uncertainty of Models for Modulus of Elasticity of Concrete in Colombian Code
Author(s):
Albert R. Ortiz and Julian Carrillo
Publication:
Materials Journal
Volume:
122
Issue:
2
Appears on pages(s):
3-14
Keywords:
aggregates; Bayesian model updating; modulus of elasticity; uncertainty quantification
DOI:
10.14359/51745620
Date:
3/1/2025
Abstract:
The modulus of elasticity of concrete is typically estimated usingnumerical models that consider factors such as the compressivestrength of the concrete, aggregate properties, unit weightof concrete, and water-cement ratio. The most-used equationdepends on the relationship between the compressive strength ofthe concrete and its modulus of elasticity. However, this simplifiedformula may provide an inaccurate estimate of the modulusof elasticity of concrete containing different types of aggregatesunder varying loading conditions. More sophisticated models canbe used to accurately estimate the modulus of elasticity for specificapplications, such as expressions involving the unit weight ofconcrete. This study presents a probabilistic update to the expressions used for estimating the modulus of elasticity of concretebased on an extensive database of over 2600 experimental testsfrom 20 different studies. Bayesian inference was used to updatethe currently proposed models, allowing for the determination ofthe expressions representing the trends of the current databasealong with their associated uncertainties. The updated expressionswere formulated considering either the compressive strength ofconcrete or both the compressive strength and the unit weight asinput parameters. Expressions for estimating the modulus of elasticity, considering the aggregate’s origin, were also updated. Thiscomprehensive approach enhances the accuracy and reliability ofpredicting the modulus of elasticity, providing valuable insightsand tools for concrete structures’ design and structural reliabilityanalysis.
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