he 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.

Meta-Analysis of Steel Fiber-Reinforced Concrete Mixtures Leads to Practical Mix Design Methodology

Ferrara, Liberato
2021-01-01

Abstract

he 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.
2021
rtificial intelligence; concrete; database; FRC; mix design; fibers; proportioning
File in questo prodotto:
File Dimensione Formato  
materials-14-03900 published version.pdf

accesso aperto

: Publisher’s version
Dimensione 2.25 MB
Formato Adobe PDF
2.25 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1180177
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 6
  • ???jsp.display-item.citation.isi??? 5
social impact