Enhancing shear strength predictions of UHPC beams through hybrid machine learning approaches.

ABSTRACT: Ultra-high-performance concrete (UHPC) beam shear strength prediction is a complicated process due to the involvement of numerous parameters. The accuracy needed for precise predictions is frequently lacking in current empirical equations and traditional machine learning (ML) techniques. This study proposes hybrid ML models that integrate three nature inspired metaheuristic algorithms-Giant Armadillo Optimization (GOA), Spotted Hyena Optimization (SHO) and Leopard seal optimization (LSA)- Extreme Gradient Boosting (XGB) to predict the shear strength of UHPC beams. A comprehensive dataset was created from extensive literature reviews and trained and tested on the models using multiple input parameters that affect UHPC's shear capacity. For model assessment, performance metrics, such as coef

SUBMITTER: Sapkota SC 

PROVIDER: S-EPMC12318006 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature