![]() In certain instances, supplementary materials like fly ash (PFA) 2, 3, blast furnace slag (GGBS) 4, silica fume 5, and other industrial waste/by-products are added in concrete to enhance the mechanical properties of the concrete 4. It also demonstrates excellent benefits over other construction materials such as steel, and concrete can be produced with minimum effort. Concrete's economic value allows it to be widely used in constructions and the accessibility to the material available in the local market. Concrete comprises four primary components: coarse aggregate, fine aggregate, cement, and water. Concrete has the characteristics of rich raw material, low price, and high compressive strength and good durability 1. 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.Ĭoncrete has been commonly used in construction and architecture due to its favourable engineering properties. Comparing all 5 BML models, the GBR model shows the highest prediction accuracy with R 2 of 0.96 and lowest model error with MAE and RMSE of 2.73 and 3.40, respectively for test dataset. Additionally, the BML models were further optimised with Random Search algorithms and compared to BML models with default hyperparameters. In these studies, the BML model’s performance is evaluated based on prediction accuracy and prediction error rates, i.e., R 2, MSE, RMSE, MAE, RMSLE, and MAPE. 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. Researchers have predicted the compressive strength of concrete for various mixes using machine learning and deep learning models. Predicting the compressive strength of concrete is a complicated process due to the heterogeneous mixture of concrete and high variable materials. ![]()
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