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- W4328111222 abstract "This study proposed an intelligent method based on hyperparameter optimization to predict the strength of the interfacial bond between fiber-reinforced polymer (FRP) and concrete. Because ordinary machine learning models extract features manually, this requires additional time and causes errors in parameter selection. High-precision machine learning model selection and automatic hyperparameter optimization can help overcome these limitations. Comparing eight different machine learning models (i.e., LReg, KNN, LR, MLP, BRR, SVR, LightGBM, and CBR), CatBoost was selected as the primary model for the hyperparameter optimization. CatBoost showed the best performance with the R2 of 0.9394 and MAPE of 1.21%. According to the prediction results, the hyperparameter optimization reduced the dispersion degree by 90 %. In general, the machine learning model works better than the existing models in terms of the coefficient of determination, root mean square error, and coefficient of variation. Furthermore, the model enhanced by the hyperparameter optimization was better than the selected CatBoost model, which indicates that hyperparameter optimization is a reliable approach to improve the accuracy of the model." @default.
- W4328111222 created "2023-03-22" @default.
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- W4328111222 date "2023-05-01" @default.
- W4328111222 modified "2023-10-13" @default.
- W4328111222 title "Hyperparameter optimization for interfacial bond strength prediction between fiber-reinforced polymer and concrete" @default.
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- W4328111222 doi "https://doi.org/10.1016/j.istruc.2023.03.082" @default.
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