Research on longitudinal reinforcement ratio prediction method of reinforced concrete single eccentric compression columns based on machine learning
Longitudinal reinforcement calculation in reinforced concrete(RC)columns is vital for ensuring the safety of RC structures.International standards provide simplified and efficient methods for this purpose.Due to inherent assumptions,these standard methods exhibit variability,necessitating improvements in accuracy and precision.This paper extensively collects global RC column test data,validating the longitudinal reinforcement calculation methods of Chinese,American,and European standards using actual material strength values.The analysis shows significant variability in reinforcement ratios under small eccentric loading across all methods.A machine learning-based approach for calculating RC column longitudinal reinforcement ratios is proposed,utilizing 530 experimental samples to train Decision Tree,Support Vector Machine,Random Forest,and Extreme Gradient Boosting models.Feature iteration based on model fit,generalization,and complexity,along with hyperparameter optimization using random search and ten-fold cross-validation,identifies the XGBoost model as optimal.SHapley Additive exPlanations(SHAP)methods and partial dependence plots elucidate the XGBoost model,visualizing the impact of feature parameters consistent with mechanical principles.Compared to standard methods,the machine learning model demonstrates higher accuracy and reduced variability,serving as an effective complement to"physics-driven"RC column design methods,offering a new avenue for more precise and efficient RC column design.
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