Recent structural collapses caused by fire have focused attention on research concerning fire safety in building design. Steel connections are an important component of any structural steel building as they provide li...Recent structural collapses caused by fire have focused attention on research concerning fire safety in building design. Steel connections are an important component of any structural steel building as they provide links between the principal structural members. Considering the importance of this matter this paper describes a spring-stiffness model developed to predict the behavior of bolted angle connections bare-steel joints at elevated temperature. The joint components are considered as springs with predefined mechanical properties i.e. stiffness and strength. The elevated temperature joint’s response can be predicted by assem-bling the stiffness of the components which are assumed to degrade with increasing temperature based on the recommendations presented in the design parameters code. Comparison of the results from the model with existing experimental data showed good agreement. The proposed model can be easily modified to describe the elevated temperature behavior of other types of joint as well as joints under large rotations.展开更多
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
This paper investigates the performance of T-stub connected semi-rigid joint of rectangular tubular columns and H-shaped steel beams.The finite element analysis software ABAQUS is used to analyze the nonlinear perform...This paper investigates the performance of T-stub connected semi-rigid joint of rectangular tubular columns and H-shaped steel beams.The finite element analysis software ABAQUS is used to analyze the nonlinear performance of the joint under monotonic loading.Meanwhile,the dimensions of T-stub,column and beam are considered as analytic parameters to discuss the performance of the joint.The analysis shows that the thickness and the length of T-stub webs,the height of beam section,bolt diameter,shear connector and the preloaded force affect the performance of the joint largely,and the thickness of the steel tube,the thickness and length of T-stub flange,bolt spacing have relatively little influences on the performance of the joint.The research results indicate that this joint is semi-rigid joint.展开更多
文摘Recent structural collapses caused by fire have focused attention on research concerning fire safety in building design. Steel connections are an important component of any structural steel building as they provide links between the principal structural members. Considering the importance of this matter this paper describes a spring-stiffness model developed to predict the behavior of bolted angle connections bare-steel joints at elevated temperature. The joint components are considered as springs with predefined mechanical properties i.e. stiffness and strength. The elevated temperature joint’s response can be predicted by assem-bling the stiffness of the components which are assumed to degrade with increasing temperature based on the recommendations presented in the design parameters code. Comparison of the results from the model with existing experimental data showed good agreement. The proposed model can be easily modified to describe the elevated temperature behavior of other types of joint as well as joints under large rotations.
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.
基金This project was supported by Shenyang Technology Development Program(F11-166-9-00)Liaoning Key Laboratory Program(LS2010134)+1 种基金Liaoning Bai Qian Wan Talents Program(2009921095)Technology Program of Ministry of Housing and Urban-Rural Development(2011-k3-23).
文摘This paper investigates the performance of T-stub connected semi-rigid joint of rectangular tubular columns and H-shaped steel beams.The finite element analysis software ABAQUS is used to analyze the nonlinear performance of the joint under monotonic loading.Meanwhile,the dimensions of T-stub,column and beam are considered as analytic parameters to discuss the performance of the joint.The analysis shows that the thickness and the length of T-stub webs,the height of beam section,bolt diameter,shear connector and the preloaded force affect the performance of the joint largely,and the thickness of the steel tube,the thickness and length of T-stub flange,bolt spacing have relatively little influences on the performance of the joint.The research results indicate that this joint is semi-rigid joint.
