Model uncertainty of numerical crack width in reinforced concrete
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F28399269%3A_____%2F26%3AN0000001" target="_blank" >RIV/28399269:_____/26:N0000001 - isvavai.cz</a>
Result on the web
<a href="https://www.researchgate.net/publication/401660433_Model_uncertainty_of_numerical_crack_width_in_reinforced_concrete" target="_blank" >https://www.researchgate.net/publication/401660433_Model_uncertainty_of_numerical_crack_width_in_reinforced_concrete</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Model uncertainty of numerical crack width in reinforced concrete
Original language description
Crack width control is essential for the serviceability of reinforced concrete (RC) structures, yet current design models rely on simplified assumptions that fail to capture strain localization and material heterogeneity. This study investigates uncertainty in crack width predictions using a finite-element modelbased on smeared crack and fracture mechanics approaches. The model incorporates the crack band concept to ensure mesh-objective energy dissipation and addresses limitations of experimental measurements, such as single-face observations and the exclusion of microcracks. Validation against experimental results of RC elements subjected to bending and direct tension examines the influence of mesh size, bar modeling, and concrete heterogeneity represented by random fields. The verification demonstrates that 3D modeling withembedded bars and an imposed minimum crack band size significantly improves prediction accuracy, achieving model uncertainty close to unity for maximum crack width. The findings provide practical guidance forapplying smeared crack models in the serviceability verification of RC structures.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
20101 - Civil 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
2026
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů