Shear Capacity Prediction and Reliability Analysis of Corroded Reinforced Concrete Beams Using Deep Generative Modeling and Ensemble Learning

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Shear Capacity Prediction and Reliability Analysis of Corroded Reinforced Concrete Beams Using Deep Generative Modeling and Ensemble Learning

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Shear Capacity Prediction and Reliability Analysis of Corroded Reinforced Concrete Beams Using Deep Generative Modeling and Ensemble Learning

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20102 - Construction engineering, Municipal and structural 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

2025

Kód důvěrnosti údajů

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

Název periodika

Engineering Applications of Artificial Intelligence

ISSN

0952-1976

e-ISSN

1873-6769

Svazek periodika

157

Číslo periodika v rámci svazku

111085

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

19

Strana od-do

—

Kód UT WoS článku

001507630700001

EID výsledku v databázi Scopus

2-s2.0-105007435601