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<p>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.</p>

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Explainable machine learning and application-oriented tool for predicting effective hoop strain of fiber-reinforced polymer-confined concrete

  • 期刊论文

  • 发表日期：

    2025年10月1日

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  • Ibrahim A. Tijani,
  • Tadesse G. Wakjira,
  • Hasan Haroglu,
  • M. Shahria Alam

摘要

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.