Stainless-steel provides substantial advantages for structural uses,though its upfront cost is notably high.Consequently,it’s vital to establish safe and economically viable design practices that enhance material uti...Stainless-steel provides substantial advantages for structural uses,though its upfront cost is notably high.Consequently,it’s vital to establish safe and economically viable design practices that enhance material utilization.Such development relies on a thorough understanding of the mechanical properties of structural components,particularly connections.This research advances the field by investigating the behavior of stainless-steel connections through the use of a four-parameter fitting technique and explainable artificial intelligence methods.Training was conducted on eight different machine learning algorithms,namely,Decision Tree,Random Forest,K-nearest neighbors,Gradient Boosting,Extreme Gradient Boosting,Light Gradient Boosting,Adaptive Boosting,and Categorical Boosting.SHapley Additive Explanations was applied to interpret model predictions,highlighting features like spacing between bolts in tension and end-plate height as highly impactful on the initial rotational stiffness and plastic moment resistance.Results showed that Extreme Gradient Boosting achieved a coefficient of determination score of 0.99 for initial stiffness and plastic moment resistance,while Gradient Boosting model had similar performance with maximum moment resistance and ultimate rotation.A user-friendly graphical user interface(GUI)was also developed,allowing engineers to input parameters and get rapid moment–rotation predictions.This framework offers a data-driven,interpretable alternative to conventional methods,supporting future design recommendations for stainless-steel beam-to-column connections.展开更多
文摘Stainless-steel provides substantial advantages for structural uses,though its upfront cost is notably high.Consequently,it’s vital to establish safe and economically viable design practices that enhance material utilization.Such development relies on a thorough understanding of the mechanical properties of structural components,particularly connections.This research advances the field by investigating the behavior of stainless-steel connections through the use of a four-parameter fitting technique and explainable artificial intelligence methods.Training was conducted on eight different machine learning algorithms,namely,Decision Tree,Random Forest,K-nearest neighbors,Gradient Boosting,Extreme Gradient Boosting,Light Gradient Boosting,Adaptive Boosting,and Categorical Boosting.SHapley Additive Explanations was applied to interpret model predictions,highlighting features like spacing between bolts in tension and end-plate height as highly impactful on the initial rotational stiffness and plastic moment resistance.Results showed that Extreme Gradient Boosting achieved a coefficient of determination score of 0.99 for initial stiffness and plastic moment resistance,while Gradient Boosting model had similar performance with maximum moment resistance and ultimate rotation.A user-friendly graphical user interface(GUI)was also developed,allowing engineers to input parameters and get rapid moment–rotation predictions.This framework offers a data-driven,interpretable alternative to conventional methods,supporting future design recommendations for stainless-steel beam-to-column connections.
