Accurate compressive strength prediction using machine learning algorithms and optimization techniques

Numerous components' complex interrelationships and interconnectedness present a formidable obstacle in developing mix designs for high-performance concrete (HPC) formulation. The effectiveness of machine learning (ML) algorithms in resolving this paradox has been illustrated. However, they are classified as opaque black-box models due to the lack of a discernible correlation between blend ratios and compressive durability. The present study proposes a semi-empirical methodology that integrates various techniques, including non-dimensionalization and optimization, to overcome this constraint. The methodology exhibits a noteworthy level of accuracy when forecasting compressive strength (CS) across a spectrum of divergent datasets, thus evincing its extensive and all-encompassing efficacy. Moreover, the precise relationship that semi-empirical equations convey is of great significance to practitioners and researchers in this field, especially with respect to their predictive abilities. The determination of CS in concrete is a critical facet of the design of HPC. An exhaustive comprehension of the intricate interplay between manifold factors is requisite to attain an ideal blend proportion. The study’s findings indicate that \(RF\) $\mathit{RF}$ can accurately predict \(CS\) $\mathit{CS}$ . Moreover, the combination of optimization algorithms significantly enhances the model’s effectiveness. Among the three Optimization Algorithms under consideration, the COA optimizer has exhibited superior performance in augmenting the accuracy and precision of the RF prediction model for CS. As a result, RFCO obtained the more suitable value of R² and RMSE obtained 0.998 and 0.88, alternatively.