Comparative Study on Hyperparameter Tuning for Predicting Concrete Compressive Strength

This study assesses the impact of hyperparameter optimization algorithms on the performance of machine learning-based concrete compressive strength prediction models. Three datasets were used to compare the performance of a basic model that had not undergone hyperparameter optimization, with the model incorporating random search, grid search, and Bayesian optimization. A post-hoc analysis using Shapley additive explanations was also conducted. The results demonstrate that the effectiveness of hyperparameter optimization varies depending on the characteristics of the dataset. For Dataset 1, the application of search algorithms appeared to improve prediction accuracy to some extent. However, for Datasets 2 and 3, the performance improvement from the search algorithms was either insignificant or decreased. Despite these contrasting results, the post-analysis showed that the influence of each feature generally aligned with empirical knowledge across all datasets, suggesting that the Shapley additive explanations method alone may have limitations in pinpointing the causes of model overfitting.