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| Naji, M. |
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| Motta, Antonella |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Kočí, Jan | Prague |
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| Azam, Siraj |
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| Ali, M. A. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Van Coile, Ruben
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Publications (9/9 displayed)
- 2024Experimental study on the thermal performance of soda-lime-silica glass by radiant panel testing
- 2023Probability density function models for float glass under mechanical loading with varying parameterscitations
- 2022Probabilistic characterization of the performance of a composite slab panel during and after fire
- 2022Experimental investigation into the effect of elevated temperatures on the fracture strength of soda-lime-silica glasscitations
- 2021Quantification of model uncertainties of the energy-based method for dynamic column removal scenarioscitations
- 2021Effects of the fire decay phase on the bending capacity of a fire-exposed reinforced concrete slab
- 2021Experimental investigation of the elastic modulus of high strength concrete at elevated temperatures
- 2018Probabilistic model for steel yield strength retention factor at elevated temperatures : influence of model choice on structural failure fragility curve for steel columns exposed to fire
- 2013Probabilistic FAD and ductile tearing assessment
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article
Probability density function models for float glass under mechanical loading with varying parameters
Abstract
Glass as a construction material has become indispensable and is still on the rise in the building industry. However, there is still a need for numerical models that can predict the strength of structural glass in different configurations. The complexity lies in the failure of glass elements largely driven by pre-existing microscopic surface flaws. These flaws are present over the entire glass surface, and the properties of each flaw vary. Therefore, the fracture strength of glass is described by a probability function and will depend on the size of the panels, the loading conditions and the flaw size distribution. This paper extends the strength prediction model of Osnes et al. with the model selection by the Akaike information criterion. This allows us to determine the most appropriate probability density function describing the glass panel strength. The analyses indicate that the most appropriate model is mainly affected by the number of flaws subjected to the maximum tensile stresses. When many flaws are loaded, the strength is better described by a normal or Weibull distribution. When few flaws are loaded, the distribution tends more towards a Gumbel distribution. A parameter study is performed to examine the most important and influencing parameters in the strength prediction model.