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.

In this research, a comparison study of the machine learning (ML) optimisation technique to predict the compressive strength of concrete is discussed. In previous studies, researchers focused on identifying the machine learning model by comparing, ensemble, bagging, and fusion methods in predicting the concrete strength. In this research, an ML model hyper-parameter optimisation is used to improve the prediction accuracy and performance of the model. Extreme gradient boosting (XGBoost) is used as the base model to perform the prediction, as the XGBoost has a built-in model ensemble, bagging, and boosting algorithms. Grid Search, Random Search, and Bayesian Optimisation are selected and used to optimise the hyperparameters of the XGBoost model. For this particular prediction study, the optimised models based on Random Search performed better than other optimisation methods. The Random Search optimisation method showed substantial improvements in prediction accuracy, modelling error and computation time.