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Failure prediction and optimal selection of adhesives for glass/steel adhesive joints

Failure prediction and optimal selection of adhesives for glass/steel adhesive joints
Failure prediction and optimal selection of adhesives for glass/steel adhesive joints
Mild steel/tempered glass adhesive joints are becoming a common occurrence in the construction industry. A numerical parametric study for adhesive property optimisation is conducted and determines strength and ductility as the main parameters affecting the joint performance. Numerical simulations include adhesive pressure-sensitivity, plasticity and failure modelling and are also used to further investigate onset and progression of damage leading to failure of the joints. Following this, the market of structural adhesives is scanned, resulting in the identification of an adhesive system that aligns with the ‘optimal’ strength and ductility parameters identified from the parametric study. The chosen adhesive system is experimentally compared and benchmarked against a brittle and a ductile adhesive in steel/glass adhesive joints subjected to four different load-cases. It is demonstrated that the proposed modelling methodology yields accurate predictions of the adhesive and adherend stress states and failure behaviour for the four different load-cases, thus highlighting the model’s ability to predict the response and failure of all three adhesives and tempered glass.
Glass structures, Glass adhesive joints, Material characterization testing, Numerical modelling
0141-0296
1-14
Katsivalis, Ioannis
d5162f7b-334f-4955-863a-75dba6f48d9b
Thomsen, Ole
f3e60b22-a09f-4d58-90da-d58e37d68047
Feih, Stefanie
993c164c-b69f-40ce-b80f-d976a9989175
Achintha, Mithila
8163c322-de6d-4791-bc31-ba054cc0e07d
Katsivalis, Ioannis
d5162f7b-334f-4955-863a-75dba6f48d9b
Thomsen, Ole
f3e60b22-a09f-4d58-90da-d58e37d68047
Feih, Stefanie
993c164c-b69f-40ce-b80f-d976a9989175
Achintha, Mithila
8163c322-de6d-4791-bc31-ba054cc0e07d

Katsivalis, Ioannis, Thomsen, Ole, Feih, Stefanie and Achintha, Mithila (2019) Failure prediction and optimal selection of adhesives for glass/steel adhesive joints. Engineering Structures, 201, 1-14, [109646]. (doi:10.1016/j.engstruct.2019.109646).

Record type: Article

Abstract

Mild steel/tempered glass adhesive joints are becoming a common occurrence in the construction industry. A numerical parametric study for adhesive property optimisation is conducted and determines strength and ductility as the main parameters affecting the joint performance. Numerical simulations include adhesive pressure-sensitivity, plasticity and failure modelling and are also used to further investigate onset and progression of damage leading to failure of the joints. Following this, the market of structural adhesives is scanned, resulting in the identification of an adhesive system that aligns with the ‘optimal’ strength and ductility parameters identified from the parametric study. The chosen adhesive system is experimentally compared and benchmarked against a brittle and a ductile adhesive in steel/glass adhesive joints subjected to four different load-cases. It is demonstrated that the proposed modelling methodology yields accurate predictions of the adhesive and adherend stress states and failure behaviour for the four different load-cases, thus highlighting the model’s ability to predict the response and failure of all three adhesives and tempered glass.

Text
KATSIVALIS_Revised_manuscrript_Engineering_Structures_Clean_Final_Version - Accepted Manuscript
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More information

Accepted/In Press date: 5 September 2019
e-pub ahead of print date: 14 October 2019
Published date: 15 December 2019
Keywords: Glass structures, Glass adhesive joints, Material characterization testing, Numerical modelling

Identifiers

Local EPrints ID: 435084
URI: http://eprints.soton.ac.uk/id/eprint/435084
ISSN: 0141-0296
PURE UUID: 570e34f9-c628-48c8-90d9-05168b147b87
ORCID for Mithila Achintha: ORCID iD orcid.org/0000-0002-1732-3514

Catalogue record

Date deposited: 22 Oct 2019 16:30
Last modified: 14 Oct 2020 04:01

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