An Intelligent prediction method for shear bearing capacity of FRP reinforced RC beams
The calculation of the shear bearing capacity of Fiber Reinforced Polymer(FRP)reinforced Reinforced Concrete(RC)beams have problems of complex formulas and tedious calculation processes.To solve these problems,ensemble learning methods were used to develop prediction models of the shear bearing capacity of Fiber Reinforced Polymer(FRP)strengthened RC beams.Experimental data were collected from relevant literature and preprocessed.RF,GBDT,and XGBoost algorithms were utilized for prediction and Bayesian optimization algorithm was used to optimize hyperparameters and compare the prediction results of different models.Results show that RF model has better prediction performance than GBDT and XGBoost models.Bayesian optimization algorithm can significantly improve the prediction performance of the models.The optimized RF model has higher prediction accuracy and generalization ability,which can provide references for research and application of FRP reinforced RC beams,offer new approaches for capacity prediction.
prediction of bearing capacityensemble learningFRP reinforced RC beamsrandom forestbayesian optimization
万方数据
维普
