Shear Capacity Prediction and Reliability Analysis of Corroded Reinforced Concrete Beams Using Deep Generative Modeling and Ensemble Learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21610%2F25%3A00384214" target="_blank" >RIV/68407700:21610/25:00384214 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.engappai.2025.111085" target="_blank" >https://doi.org/10.1016/j.engappai.2025.111085</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.engappai.2025.111085" target="_blank" >10.1016/j.engappai.2025.111085</a>
Alternative languages
Result language
angličtina
Original language name
Shear Capacity Prediction and Reliability Analysis of Corroded Reinforced Concrete Beams Using Deep Generative Modeling and Ensemble Learning
Original language description
This study develops a machine learning framework to predict the shear capacity of corroded reinforced concrete (CRC) beams, enhancing structural reliability assessments. A Variational Autoencoder (VAE) generated a synthetic dataset of 10,000 samples, addressing the challenges of limited and varied experimental data on corrosion. Comparative analyses showed the VAE outperformed Generative Adversarial Networks (GANs) based on entropy, Kullback-Leibler divergence, and Fr & eacute;chet Inception Distance (FID),indicating higher data quality and realism. Five machine learning models-Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Categorical Boosting (CatBoost), Adaptive Boosting (AdaBoost), and a back-propagation neural network (BPNN)-were trained using this data. XGBoost demonstrated superior accuracy, achieving an R2 of 0.96 on synthetic data and 0.85 on real data. Shapley Additive Explanations (SHAP) identified critical factors such as concrete strength and stirrup corrosion, impacting shear capacity. A reliability-based approach calibrated a global resistance factor of 1.10, ensuring CRC beams designed with the XGBoost model meet a reliability index of 3.8. This approach significantly advances predictive capabilities and reliability assessments for aging infrastructure management.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
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
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Engineering Applications of Artificial Intelligence
ISSN
0952-1976
e-ISSN
1873-6769
Volume of the periodical
157
Issue of the periodical within the volume
111085
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
19
Pages from-to
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UT code for WoS article
001507630700001
EID of the result in the Scopus database
2-s2.0-105007435601