In order to better predict the compressive strength of recycled coarse aggregate concrete,a compressive strength prediction model for recycled coarse aggregate concrete based on extreme gradient boosting(XGBoost)algorithm was proposed.Taking the recycled coarse aggregate concrete database as the research data set,the data set was preprocessed,and the Bayesian optimization(BO)method was used to estimate the tree-structured parzen estimator(TPE)to optimize the model parameters.The comparative verification of compressive strength prediction models for recycled coarse aggregate concrete was carried out through examples.The results show that data preprocessing and TPE-BO hyperparameter optimization methods can both improve model performance to a certain extent.Compared with random forest,K-nearest neighbor regression,support vector machine regression,and gradient boosting decision tree models,the proposed model has higher prediction accuracy and generalization ability.The high performance compressive strength prediction model provides a basis for the research and practice of recycled coarse aggregate concrete,and also provides a new approach for predicting the performance of recycled concrete.
维普
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