Refining Crack Width Predictions in RC Beams Using FEM and Neural Network-Based Surrogate Models for Crack Band Size Estimation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F28399269%3A_____%2F26%3AN0000002" target="_blank" >RIV/28399269:_____/26:N0000002 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.taylorfrancis.com/books/9781003660026/chapters/10.1201/9781003660026-76" target="_blank" >https://www.taylorfrancis.com/books/9781003660026/chapters/10.1201/9781003660026-76</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Refining Crack Width Predictions in RC Beams Using FEM and Neural Network-Based Surrogate Models for Crack Band Size Estimation

Popis výsledku v původním jazyce

Concrete cracking is analyzed using nonlinear fracture-mechanics-based constitutive models within a finite element framework. In such simulations, the predicted behavior of reinforced concrete is highly sensitive to the assumed crack spacing or crack band size, particularly when relatively large finite elements are employed. To alleviate this limitation, the present study introduces an approach in which artificial neural network surrogate models are used to estimate the crack spacing in reinforced concrete structures. Model uncertainties in terms of mean and maximum crack width are evaluated against a database of laboratory tests. The influence of reinforcement layout, geometric simplifications and mesh discretization on these uncertainties is examined. Overall, the proposed modelling strategy introduces an advanced tool for assessment of crack widths and mainly crack spacing in reinforced concrete structures at the serviceability limit state.

Název v anglickém jazyce

Refining Crack Width Predictions in RC Beams Using FEM and Neural Network-Based Surrogate Models for Crack Band Size Estimation

Popis výsledku anglicky

Concrete cracking is analyzed using nonlinear fracture-mechanics-based constitutive models within a finite element framework. In such simulations, the predicted behavior of reinforced concrete is highly sensitive to the assumed crack spacing or crack band size, particularly when relatively large finite elements are employed. To alleviate this limitation, the present study introduces an approach in which artificial neural network surrogate models are used to estimate the crack spacing in reinforced concrete structures. Model uncertainties in terms of mean and maximum crack width are evaluated against a database of laboratory tests. The influence of reinforcement layout, geometric simplifications and mesh discretization on these uncertainties is examined. Overall, the proposed modelling strategy introduces an advanced tool for assessment of crack widths and mainly crack spacing in reinforced concrete structures at the serviceability limit state.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

20101 - Civil engineering

Projekt

<a href="/cs/project/GA24-10892S" target="_blank" >GA24-10892S: Strojové učení pro víceúrovňové modelování prostorové variability a trhlin pro zajištění udržitelnosti betonových konstrukcí</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2026

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů