Predicting the compressive strength of ultra-high-performance concrete using a decision tree machine learning model enhanced by the integration of two optimization meta-heuristic algorithms

Abstract

The compressive strength (CS) of ultra-high-performance concrete (UHPC) hinges upon the distinct properties, quantities, and types of its constituent materials. To empirically decipher this intricate relationship, employing machine learning (ML) algorithms becomes indispensable. Among these, the decision tree (DT) stands out, adept at constructing a predictive model aligned with experimental datasets. Notably, these models demonstrate commendable accuracy, effectively paralleling experimental findings as a testament to DT’s efficacy in UHPC prediction based on input parameters. To elevate predictive precision, this study integrates two meta-heuristic algorithms: the Sea-horse Optimizer (SHO) and the Crystal Structure Algorithm (CryStAl). This integration spawns three hybrid models: DTSH, DTCS, and DT. Particularly, the DTSH model shines with remarkable R2 values, registering an impressive 0.997, coupled with an optimal RMSE of 1.746 during the training phase. This underlines the model’s unmatched predictive and generalization capabilities, setting it apart from other models cultivated in this research. In essence, the fusion of empirical experimentation, advanced ML via DT, and the strategic infusion of SHO and CryStAl, culminates in the ascension of predictive prowess within the realm of UHPC compressive strength projection.