The reliable prediction of hoop strain of fiber-reinforced polymer(FRP)-confined concrete is crucial for assessing confinement efficiency and ensuring structural integrity.Existing empirical models often fall short as...The reliable prediction of hoop strain of fiber-reinforced polymer(FRP)-confined concrete is crucial for assessing confinement efficiency and ensuring structural integrity.Existing empirical models often fall short as a result of idealized assumptions and limited generalizability across diverse materials and geometries.This study presents a novel,data-driven machine learning(ML)approach to estimate the effective hoop strain of FRP-confined circular concrete columns.A refined database comprising 309 experimental specimens,including Carbon,glass,and aramid FRPs,was used.Eight ML algorithms,encompassing both single(K-Nearest Neighbors,Kernel Ridge Regression,Support Vector Regression,Decision Tree)and ensemble(AdaBoost,Gradient Boosting Machine,Extreme Gradient Boosting,Random Forest)models,were trained and optimized using Optuna with 10-fold cross-validation.The top-performing models have coefficient of determination of greater than 95%as well as low residual variance and error on the full data set.Accordingly,SHapley Additive exPlanations were incorporated for global and local interpretability of the model predictions.The best-performing model was deployed in a user-friendly graphical interface,aiding an accurate and interpretable tool for practitioners.The proposed framework significantly outperforms conventional empirical models,offering a scalable solution for assessing hoop strain of FRP-confined concrete.展开更多
文摘The reliable prediction of hoop strain of fiber-reinforced polymer(FRP)-confined concrete is crucial for assessing confinement efficiency and ensuring structural integrity.Existing empirical models often fall short as a result of idealized assumptions and limited generalizability across diverse materials and geometries.This study presents a novel,data-driven machine learning(ML)approach to estimate the effective hoop strain of FRP-confined circular concrete columns.A refined database comprising 309 experimental specimens,including Carbon,glass,and aramid FRPs,was used.Eight ML algorithms,encompassing both single(K-Nearest Neighbors,Kernel Ridge Regression,Support Vector Regression,Decision Tree)and ensemble(AdaBoost,Gradient Boosting Machine,Extreme Gradient Boosting,Random Forest)models,were trained and optimized using Optuna with 10-fold cross-validation.The top-performing models have coefficient of determination of greater than 95%as well as low residual variance and error on the full data set.Accordingly,SHapley Additive exPlanations were incorporated for global and local interpretability of the model predictions.The best-performing model was deployed in a user-friendly graphical interface,aiding an accurate and interpretable tool for practitioners.The proposed framework significantly outperforms conventional empirical models,offering a scalable solution for assessing hoop strain of FRP-confined concrete.
