Prediction of FRP-concrete interfacial bond strength based on machine learning.

• Established a database with 1375 FRP-concrete direct shear test specimens. • Analyzing the correlation of six variables revealed that five factors affecting the FRP-concrete interfacial shear capacity most. • Six machine learning models substantially increased the prediction accuracy compared with the sixteen traditional empirical equations, and they especially reduced the variation. • Based on the ANN algorithm, an accurate, explicit and practical equation was derived to predict the FRP-concrete interfacial shear capacity. Externally bonding fiber reinforced polymer (FRP) to concrete structures is an effective way to enhance the mechanical performance of concrete structures. Many equations have been proposed to predict the interfacial bond strength for FRP-concrete structures but have limited accuracy due to the complexity of the bond behavior. This study proposes to formulate the FRP-concrete interfacial bond strength based on machine learning (ML) methods, which have emerged as a promising alternative to achieve high prediction accuracy in high-dimension problems. To this end, a database containing 1,375 FRP-concrete direct shear test specimens that failed due to interfacial debonding was established. The database was improved using an unsupervised isolation forest that identified and eliminated anomalous data, and was then used to train six ML models, namely artificial neural networks (ANN), support vector machine, decision tree, gradient boosting decision tree, random forest, and XGboost algorithms, to predict the FRP-concrete interfacial bond strength. The ML predictive models showed higher accuracy than 16 existing equations in the literature. The XGBoost model showed the highest accuracy, and its coefficient of variation was 54% lower than the existing equation with the highest accuracy among those considered. The ANN model was used to perform a parametric study on the influencing parameters, and a new equation was generated to predict the interfacial bond strength, considering the key influencing parameters. The equation enables interpretation of the ML models. The study combines ML models and traditional physical models to achieve a novel, interpretable ML method for predicting FRP-concrete interfacial bond strength. [ABSTRACT FROM AUTHOR]