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
现场测试数据表明HDPE双壁波纹管道在填土施工时会产生较大装配应力和应变。然而,目前关于HDPE管道服役期力学性能的研究和设计方法均忽略装配效应对管道力学特性的影响,高估了管道服役性能并带来安全隐患。通过现场试验,对管径600 mm H...现场测试数据表明HDPE双壁波纹管道在填土施工时会产生较大装配应力和应变。然而,目前关于HDPE管道服役期力学性能的研究和设计方法均忽略装配效应对管道力学特性的影响,高估了管道服役性能并带来安全隐患。通过现场试验,对管径600 mm HDPE双壁波纹管道填土施工过程中产生的径向挠度与管周环向应变进行实时监测;结果表明,管道施工填土产生的最大装配应变发生在管侧(与管轴线等深度的管壁处),而管顶和管侧挠度近似相等;管顶挠度与填土高度和最大管周环向应变之间均存在良好线性关系。通过有限元数值模拟分析并综合现场试验数据,提出了基于填土高度的管顶挠度预测公式和基于管顶挠度的最大管周环向应变预测公式,可以方便快捷地预测HDPE管道装配应变。通过对比报道的两个现场试验的实测数据验证所得公式,结果表明所得公式预测值与实测管顶挠度的误差范围为7%~13%,表明该公式可准确计算施工填土时HDPE管道的管顶挠度。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.
文摘现场测试数据表明HDPE双壁波纹管道在填土施工时会产生较大装配应力和应变。然而,目前关于HDPE管道服役期力学性能的研究和设计方法均忽略装配效应对管道力学特性的影响,高估了管道服役性能并带来安全隐患。通过现场试验,对管径600 mm HDPE双壁波纹管道填土施工过程中产生的径向挠度与管周环向应变进行实时监测;结果表明,管道施工填土产生的最大装配应变发生在管侧(与管轴线等深度的管壁处),而管顶和管侧挠度近似相等;管顶挠度与填土高度和最大管周环向应变之间均存在良好线性关系。通过有限元数值模拟分析并综合现场试验数据,提出了基于填土高度的管顶挠度预测公式和基于管顶挠度的最大管周环向应变预测公式,可以方便快捷地预测HDPE管道装配应变。通过对比报道的两个现场试验的实测数据验证所得公式,结果表明所得公式预测值与实测管顶挠度的误差范围为7%~13%,表明该公式可准确计算施工填土时HDPE管道的管顶挠度。
