ArticleScientific reports2025
ML modeling of ultimate and relative bond strength for corroded rebars based on concrete and steel properties.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Corrosion-induced bond strength degradation between reinforcing bars and concrete significantly compromises the durability and structural performance of reinforced concrete (RC) systems. Accurate prediction of bond behavior under corrosion is critical for assessing residual capacity and informing rehabilitation strategies. This study proposes a data-driven framework for predicting both Ultimate Bond Strength (UBS) and Relative Bond Strength (RBS) of corroded reinforcement using a multi-output machine learning (ML) approach. A comprehensive dataset was compiled from experimental studies, and six ML algorithms, Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GBoost), and Extreme Gradient Boosting (XGBoost), were trained to forecast UBS and RBS simultaneously. Among them, XGBoost exhibited the highest predictive performance, achieving R
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