Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/44731
Title: Analytical and numerical investigation of adhesively bonded perfobond shear connectors for timber-concrete composite floors
Authors: APPAVURAVTHER SUMICHRAST, Elif Tuba 
VANDOREN, Bram 
GOUVEIA HENRIQUES, Jose 
Issue Date: 2024
Publisher: ELSEVIER SCIENCE INC
Source: Structures, 70 (Art N° 107582)
Abstract: In timber-concrete composites (TCC), more researchers are trying to find the "ultimate" shear connector, which combines multiple advantages. Bonded steel plates are one of the possible solutions. This paper provides an analytical and a numerical approach, calibrated with previously conducted experiments, to predict the load carrying capacity, and corresponding governing failure mode, of perfobond shear connections in TCC beams. The connection's mechanical model includes all possible failure modes which can limit the connection strength based on load transfer mechanism. Accordingly, failure modes are first identified and their resistance assessed using strength models available in the literature. Subsequently, the assembly of these determines the connection resistance and the governing failure mode. Then, a three dimensional finite element model is created in Abaqus to deepen the connection's mechanical behaviour and further validate the proposed analytical approach. Finally, a parametric study is conducted with the aim of achieving a ductile behaviour of the connections. The accuracy of the proposed analytical model is assessed with the parametric study.
Notes: Appavuravther, E (corresponding author), Hasselt Univ, Fac Engn Technol, CERG, Hasselt, Belgium.
eliftuba.appavuravther@uhasselt.be
Keywords: Adhesively bonded connection;Analytical model;3D finite element model;Perfobond connector;Timber-concrete composite
Document URI: http://hdl.handle.net/1942/44731
ISSN: 2352-0124
e-ISSN: 2352-0124
DOI: 10.1016/j.istruc.2024.107582
ISI #: 001353719100001
Rights: 2024 Institution of Structural Engineers. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

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