Optimizing Gene Expression Programming to Predict Shear Capacity in Corrugated Web Steel Beams

Corrugated web steel systems, such as corrugated web girders (CWG) and beams (CWSB), have the potential to influence the modern construction industry due to their unique properties, including enhanced shear strength and reduced necessity for transverse stiffeners. Nevertheless, the lack of a rapid and accurate design approach still limits its wide applications. Recently, gene expression programming (GEP) has been employed to predict the shear capacity of cold-formed steel channels, demonstrating superior predictive accuracy and compliance with established standards. This study applies GEP to predict the shear capacity of sinusoidal CWSBs and optimizes its predictive performance by employing a systematic grid search to explore combinations of chromosomes, head sizes, gene counts, and linking functions. The process involved testing ۱۹ different parameter combinations and more than ۶۰ developed models. The findings include the sensitivity of the model's performance to gene count and the critical role of the linking function. The optimal model in the study, GEP۱۳, achieved R² of ۰.۹۵, an RMSE of ۱۰۰.۵, and an MAE of ۸۶.۶ in the testing dataset with ۱۵۰ chromosomes, a head size of ۱۲, and four genes using a multiplication linking function. Doi: ۱۰.۲۸۹۹۱/CEJ-۲۰۲۴-۰۱۰-۰۵-۰۲ Full Text: PDF

Keywords:

Sinusoidal Steel Beam , SCWBS , ANN , Shear Strength Analysis , Network Topology , Predictive Modelling , Hyperparameter Optimization , Geometric Properties.