Resistance Model Uncertainty in Non-Linear Numerical Analyses of Ultra-High-Performance Reinforced Concrete Beams in Flexure
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21610%2F24%3A00378077" target="_blank" >RIV/68407700:21610/24:00378077 - isvavai.cz</a>
Result on the web
<a href="https://www.ctresources.info/ccc/pub.html?f=v9cst24" target="_blank" >https://www.ctresources.info/ccc/pub.html?f=v9cst24</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.4203/ccc.9.9.3" target="_blank" >10.4203/ccc.9.9.3</a>
Alternative languages
Result language
angličtina
Original language name
Resistance Model Uncertainty in Non-Linear Numerical Analyses of Ultra-High-Performance Reinforced Concrete Beams in Flexure
Original language description
This study presents the bending resistance model uncertainty and corresponding partial factors when performing a design or an assessment of ultra-high-performance reinforced concrete (UHPC) beams via non-linear finite element analyses (NLFEA). UHPC beams that have been both experimentally tested and simulated via NLFEA are considered, as documented in the literature, treating each source as presenting a unique modelling hypothesis of the beams' bending behaviour. A probabilistic analysis through Bayesian updating processes these uncertainties, updating prior distributions of resistance model uncertainty with data from various modelling hypothesis to estimate posterior distributions and the final average posterior distribution. The coefficient of variation and mean value of the average posterior distribution is used to calibrate corresponding partial factors in accordance with the the global safety format for NLFEAs proposed by codes and literature.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20102 - Construction engineering, Municipal and structural engineering
Result continuities
Project
<a href="/en/project/GA24-10892S" target="_blank" >GA24-10892S: Machine Learning for Multiscale Modelling of Spatial Variability and Fracture for Sustainable Concrete Structures</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the Fifteenth International Conference on Computational Structures Technology
ISBN
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ISSN
2753-3239
e-ISSN
2753-3239
Number of pages
11
Pages from-to
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Publisher name
Civil-Comp Press
Place of publication
Edinburgh
Event location
Praha
Event date
Sep 4, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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