Predicting Strength of High-Performance Concrete Using Gradient Boosting Machine Learning: A Comparative Analysis Between Manual and Grid Search Cross-Validation Hyperparameter Tuning

This study evaluates the effectiveness of a gradient boosting regression model in forecasting concrete strength by comparing three hyperparameter configurations: default settings, manual tuning, and automated Grid Search CV. A publicly available dataset of 1030 concrete mixes, featuring cement, slag, fly ash, water, superplasticiser, coarse and fine aggregates, and concrete age, was divided into an 80-20 train-test split. Model performance was assessed using mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and the coefficient of determination (R<sup>2</sup>). The default model achieved R<sup>2</sup> = 0.89, while targeted adjustments to the parameters, such as the number of estimators, raised R<sup>2</sup> to 0.93. Manual fine-tuning of all hyperparameters simultaneously produced the best results of R<sup>2</sup> = 0.94, marginally outperforming Grid Search CV 3- and 5-fold of R<sup>2</sup> = 0.93. The number of estimators was identified as the most influential parameter. Although exhaustive grid search offers systematic optimisation with high runtimes, manual finetuning can yield superior accuracy within a constrained parameter space.