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Experimental investigation and artificial neural network based prediction of bond strength in self-compacting geopolymer concrete reinforced with Basalt FRP bars

Rahman, Sherin Khadeeja and Al-Ameri, Riyadh 2021, Experimental investigation and artificial neural network based prediction of bond strength in self-compacting geopolymer concrete reinforced with Basalt FRP bars, Applied sciences, vol. 11, no. 11, pp. 1-25, doi: 10.3390/app11114889.

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Title Experimental investigation and artificial neural network based prediction of bond strength in self-compacting geopolymer concrete reinforced with Basalt FRP bars
Author(s) Rahman, Sherin Khadeeja
Al-Ameri, RiyadhORCID iD for Al-Ameri, Riyadh orcid.org/0000-0003-1881-1787
Journal name Applied sciences
Volume number 11
Issue number 11
Article ID 4889
Start page 1
End page 25
Total pages 25
Publisher MDPI AG
Place of publication Basel, Switzerland
Publication date 2021-06-01
ISSN 2076-3417
Keyword(s) self-compacting geopolymer concrete
basalt
fibre-reinforced polymer
pull-out test
bond strength prediction
ANN model
Summary The current research on concrete and cementitious materials focuses on finding sustainable solutions to address critical issues, such as increased carbon emissions, or corrosion attack associated with reinforced concrete structures. Geopolymer concrete is considered to be an eco-friendly alternative due to its superior properties in terms of reduced carbon emissions and durability. Similarly, the use of fibre-reinforced polymer (FRP) bars to address corrosion attack in steel-reinforced structures is also gaining momentum. This paper investigates the bond performance of a newly developed self-compacting geopolymer concrete (SCGC) reinforced with basalt FRP (BFRP) bars. This study examines the bond behaviour of BFRP-reinforced SCGC specimens with variables such as bar diameter (6 mm and 10 mm) and embedment lengths. The embedment lengths adopted are 5, 10, and 15 times the bar diameter (db), and are denoted as 5 db, 10 db, and 15 db throughout the study. A total of 21 specimens, inclusive of the variable parameters, are subjected to direct pull-out tests in order to assess the bond between the rebar and the concrete. The result is then compared with the SCGC reinforced with traditional steel bars, in accordance with the ACI 440.3R-04 and CAN/CSA-S806-02 guidelines. A prediction model for bond strength has been proposed using artificial neural network (ANN) tools, which contributes to the new knowledge on the use of Basalt FRP bars as internal reinforcement in an ambient-cured self-compacting geopolymer concrete.
Language eng
DOI 10.3390/app11114889
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2021, The Author(s)
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30152128

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.