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Title: | Experimental and analytical characterisation of hybrid timber-glass diaphragms with integrated photovoltaics | Authors: | ENGELEN, Tine BYLOOS, Dries GOUVEIA HENRIQUES, Jose DAENEN, Michael Kozłowski, Marcin VANDOREN, Bram |
Issue Date: | 2025 | Publisher: | Elsevier | Source: | Engineering structures, 343 (Art N° 121058) | Abstract: | This study investigates the structural performance of hybrid timber-glass frame walls designed to enhance racking resistance in façades, with a specific focus on the integration of photovoltaic solar cells within structural glass elements. The effects of shear loads on the system’s components and the applicability of analytical design methods are evaluated. To achieve this, eight diaphragms (1.2 × 1.2 m) were tested under in-plane shear loading using two different structural silicone adhesives, with or without a tie-down anchoring of the leading stud. A variety of measurement techniques, including displacement sensors (LVDTs), digital image correlation, fibre Bragg gratings and strain gauges were simultaneously employed to analyse the behaviour of the different components. The specimens primarily failed due to adhesive rupture. It is shown that adding wall anchorage increases the system’s racking stiffness by 30%. This study offers insights into the strains measured on the glass and solar cells during mechanical in-plane shear load tests. Furthermore, an analytical design method based on the relevant Eurocode (prEN 1995-1-1) and spring models is proposed and compared with the experimental results. The findings reveal that while this method tends to underestimate the stiffness of the wall elements, it provides an accurate prediction of the minimum load-bearing capacity. | Keywords: | Timber;Glass;Adhesive;In-plane shear test;Racking resistance;Analytical model | Document URI: | http://hdl.handle.net/1942/46640 | ISSN: | 0141-0296 | e-ISSN: | 1873-7323 | DOI: | 10.1016/j.engstruct.2025.121058 | Rights: | 2025 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 |
Files in This Item:
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Tine_Engelen_Paper_experiments_accepted_version.pdf Until 2026-08-31 | Peer-reviewed author version | 31.22 MB | Adobe PDF | View/Open Request a copy |
1-s2.0-S014102962501449X-main.pdf Restricted Access | Published version | 3.46 MB | Adobe PDF | View/Open Request a copy |
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