ArticleScientific reports2025
Enhancing shear strength predictions of UHPC beams through hybrid machine learning approaches.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Predicting carbonation depth in fiber-reinforced ultra-high performance concrete (FR-UHPC) using state-of-the-art machine learning techniques.Scientific reports · 2026Article
- Machine learning models for mechanical properties prediction of basalt fiber-reinforced concrete incorporating graphical user interface.Scientific reports · 2025Article
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7 authors.
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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 coefficient of determination (R
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