Integrating machine learning and symbolic regression for predicting damage initiation in hybrid FRP bolted connections.

ABSTRACT: The increasing adoption of machine learning (ML) in fiber-reinforced polymer (FRP) composite design has led to a reliance on black-box models, which achieve high predictive accuracy but lack interpretability. Python symbolic regression (PySR) offers a solution by deriving explicit equations that reveal the governing mechanics of composite structures. This study focuses on hybrid FRP bolted connections, which are rapidly adopted in the industry but remain insufficiently addressed in academic research. To address this gap, a framework was developed to identify key design parameters and predict damage initiation loads by integrating experimental testing, finite element modeling (FEM), and ML. Feature selection and ML models analyzed the dataset, providing insights that guided PySR in deriving

SUBMITTER: Sorour SS 

PROVIDER: S-EPMC12117092 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature