Water sorptivity prediction model for concrete with all coarse recycled concrete aggregates

Ariel Miguel Aragoncillo; Douglas Cleary; Umashanger Thayasivam; Gilson Lomboy

doi:10.1016/j.conbuildmat.2023.132128

Water sorptivity prediction model for concrete with all coarse recycled concrete aggregates

Ariel Miguel Aragoncillo
, Douglas Cleary
, Umashanger Thayasivam
, Gilson Lomboy

Rowan University

Research output: Contribution to journal › Article › peer-review

Abstract

This paper examines the water sorptivity of recycled aggregate concrete (RAC) and shows how sorptivity can be predicted using electrical resistivity as the main predictor. The measurements in 48 RAC specimens show that RAC has higher water sorptivity and lower electrical resistivity than regular concrete. RAC sorptivity and rate of saturation prediction models were developed using multiple linear regression analysis. The prediction models include electrical resistivity, percent volume of coarse recycled concrete aggregates, and air content of residual mortar as explanatory variables. A graphical calculating device based on the sorptivity regression models was also created.

Original language	English (US)
Article number	132128
Journal	Construction and Building Materials
Volume	394
DOIs	https://doi.org/10.1016/j.conbuildmat.2023.132128
State	Published - Aug 29 2023

ASJC Scopus subject areas

Civil and Structural Engineering
Building and Construction
General Materials Science

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10.1016/j.conbuildmat.2023.132128

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title = "Water sorptivity prediction model for concrete with all coarse recycled concrete aggregates",

abstract = "This paper examines the water sorptivity of recycled aggregate concrete (RAC) and shows how sorptivity can be predicted using electrical resistivity as the main predictor. The measurements in 48 RAC specimens show that RAC has higher water sorptivity and lower electrical resistivity than regular concrete. RAC sorptivity and rate of saturation prediction models were developed using multiple linear regression analysis. The prediction models include electrical resistivity, percent volume of coarse recycled concrete aggregates, and air content of residual mortar as explanatory variables. A graphical calculating device based on the sorptivity regression models was also created.",

author = "Aragoncillo, \{Ariel Miguel\} and Douglas Cleary and Umashanger Thayasivam and Gilson Lomboy",

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AU - Aragoncillo, Ariel Miguel

AU - Cleary, Douglas

AU - Thayasivam, Umashanger

AU - Lomboy, Gilson

PY - 2023/8/29

Y1 - 2023/8/29

N2 - This paper examines the water sorptivity of recycled aggregate concrete (RAC) and shows how sorptivity can be predicted using electrical resistivity as the main predictor. The measurements in 48 RAC specimens show that RAC has higher water sorptivity and lower electrical resistivity than regular concrete. RAC sorptivity and rate of saturation prediction models were developed using multiple linear regression analysis. The prediction models include electrical resistivity, percent volume of coarse recycled concrete aggregates, and air content of residual mortar as explanatory variables. A graphical calculating device based on the sorptivity regression models was also created.

AB - This paper examines the water sorptivity of recycled aggregate concrete (RAC) and shows how sorptivity can be predicted using electrical resistivity as the main predictor. The measurements in 48 RAC specimens show that RAC has higher water sorptivity and lower electrical resistivity than regular concrete. RAC sorptivity and rate of saturation prediction models were developed using multiple linear regression analysis. The prediction models include electrical resistivity, percent volume of coarse recycled concrete aggregates, and air content of residual mortar as explanatory variables. A graphical calculating device based on the sorptivity regression models was also created.

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UR - https://www.scopus.com/pages/publications/85162873546#tab=citedBy

U2 - 10.1016/j.conbuildmat.2023.132128

DO - 10.1016/j.conbuildmat.2023.132128

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SN - 0950-0618

VL - 394

JO - Construction and Building Materials

JF - Construction and Building Materials

M1 - 132128

ER -

Water sorptivity prediction model for concrete with all coarse recycled concrete aggregates

Abstract

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