Garcia-Taengua, E orcid.org/0000-0003-2847-5932, Bakhshi, M and Ferrara, L (2021) Meta-Analysis of Steel Fiber-Reinforced Concrete Mixtures Leads to Practical Mix Design Methodology. Materials, 14 (14). 3900. p. 3900. ISSN 1996-1944
Abstract
The analysis of hundreds of SFRC mixtures compiled from papers published over the last 20 years is reported. This paper is focused on the relationships between the size and dosage of steel fibers and the relative amounts of the constituents of SFRC mixtures. Multiple linear regression is applied to the statistical modeling of such relationships, leading to four equations that show considerable accuracy and robustness in estimating SFRC mixture proportions as a function of fiber content and dimensions, maximum aggregate size, and water-to-cement ratio. The main trends described by these equations are discussed in detail. The importance of the interactions between aggregates, supplementary cementitious materials, and fibers in proportioning SFRC mixtures, as well as implications for workability and stability, are emphasized. The simplicity of these data-driven equations makes them a valuable tool to guide the proportioning of SFRC mixtures. Their predictive performance when used together as a data-driven mix design methodology is confirmed using a validation dataset.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | artificial intelligence; concrete; database; FRC; mix design; fibers; proportioning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Civil Engineering (Leeds) |
Funding Information: | Funder Grant number ACI Foundation (American Concrete Institute) Not Known |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Jul 2021 14:18 |
Last Modified: | 19 Jul 2021 14:18 |
Status: | Published |
Publisher: | MDPI |
Identification Number: | 10.3390/ma14143900 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:176182 |