ArticleScientific reports2025
Predicting flexural strength of hybrid FRP-steel reinforced beams using symbolic regression and ML techniques.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Hybrid fiber-reinforced polymer (FRP) and steel reinforced concrete (hybrid FRP-steel RC) beams have gained recognition for their exceptional flexural performance, surpassing that of beams reinforced exclusively with FRP bars (FRP-RC). However, current design guidelines, such as ACI 440.11-22, fail to accurately predict the flexural strength of these hybrid systems. This study aims to enhance the predictive accuracy and interpretability of flexural strength models by applying advanced computational approaches-specifically, machine learning (ML) techniques and symbolic regression. A robust dataset of 134 experimental data points was utilized to develop predictive models. The prediction results showed that both ML and symbolic regression models significantly outperformed the ACI 440.11-22 equations, achieving lower errors (MAE, MAPE, RMSE) and higher accuracy (R
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