| Title: | Updating of a deterministic model for the free thermal strain of concrete in fire during heating and cooling using novel experimental data and Bayesian inference |
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| Authors: | ID Put, Florian (Author)
ID Felicetti, Roberto (Author)
ID Lucherini, Andrea (Author)
ID Merci, Bart (Author)
ID Van Coile, Ruben (Author) |
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| Files: |  URL - Source URL, visit https://doi.org/10.1016/j.firesaf.2026.104659
 PDF - Presentation file. (1,45 MB, This file will be accessible after 18.02.2028)
MD5: 1C3C2BF00CE7741C2FED2A90B894382B
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| Language: | English |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: |  ZAG - Slovenian National Building and Civil Engineering Institute
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| Abstract: | With the rise of performance-based structural fire design, understanding the behaviour of materials during both the heating and cooling phases of a fire has become essential. Traditionally, research has focused on the heating phase, resulting in limited data on material properties during cooling, in particular for the free thermal strain of concrete. This gap is critical, as free thermal strain significantly influences load redistribution in reinforced concrete structures. Two experimental campaigns were conducted to expand the available data: one using the UGent HIFREP (‘High Intensity Fast-Response Electric Radiant Panel’) and the other employing an electric furnace. The first test campaign provided an extensive dataset on the residual thermal strain, whereas the time- consuming furnace tests provided data for the entire fire event (heating and cooling). These datasets were used to update an existing model through Bayesian inference, coherently integrating the new information. The outcome is a comprehensive probabilistic model that accurately captures the free thermal strain behaviour of concrete throughout both heating and cooling, allowing for a full reliability-based evaluation of concrete structures in fire. |
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| Keywords: | concrete, cooling, free thermal strain, fire safety, structural fire engineering, experiments, bayesian inference |
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| Publication status: | Published |
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| Publication version: | Author Accepted Manuscript |
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| Publication date: | 18.02.2026 |
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| Publisher: | Elsevier |
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| Year of publishing: | 2026 |
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| Number of pages: | str. 1-11 |
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| Numbering: | Vol. 161, [article no.] 104276 |
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| PID: | 20.500.12556/DiRROS-28086  |
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| UDC: | 620.1/.2 |
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| ISSN on article: | 1873-7226 |
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| DOI: | 10.1016/j.firesaf.2026.104659 |
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| COBISS.SI-ID: | 270827011 |
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| Copyright: | © 2026 Elsevier Ltd |
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| Publication date in DiRROS: | 10.03.2026 |
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| Views: | 79 |
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| Downloads: | 13 |
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