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• W4281754927 abstract "Predicting the compressive strength of concrete is a complicated process due to the heterogeneous mixture of concrete and high variable materials. Researchers have predicted the compressive strength of concrete for various mixes using machine learning and deep learning models. In this research, compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement is predicted using boosting machine learning (BML) algorithms, namely, Light Gradient Boosting Machine, CatBoost Regressor, Gradient Boosting Regressor (GBR), Adaboost Regressor, and Extreme Gradient Boosting. In these studies, the BML model's performance is evaluated based on prediction accuracy and prediction error rates, i.e., R2, MSE, RMSE, MAE, RMSLE, and MAPE. Additionally, the BML models were further optimised with Random Search algorithms and compared to BML models with default hyperparameters. Comparing all 5 BML models, the GBR model shows the highest prediction accuracy with R2 of 0.96 and lowest model error with MAE and RMSE of 2.73 and 3.40, respectively for test dataset. In conclusion, the GBR model are the best performing BML for predicting the compressive strength of concrete with the highest prediction accuracy, and lowest modelling error." @default.
• W4281754927 created "2022-06-13" @default.
• W4281754927 creator A5010789167 @default.
• W4281754927 creator A5084282976 @default.
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• W4281754927 date "2022-06-09" @default.
• W4281754927 modified "2023-10-04" @default.
• W4281754927 title "Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms" @default.
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• W4281754927 doi "https://doi.org/10.1038/s41598-022-12890-2" @default.
• W4281754927 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35680937" @default.
• W4281754927 hasPublicationYear "2022" @default.
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