This paper presents a design-variable-based inelastic hysteretic model for beam-column connections. It has been well known that the load-carrying capacity of connections heavily depends on the types and design variables even in the same connection type. Although many hysteretic connection models have been proposed, most of them are dependent on the specific connection type with presumed failure mechanisms. The proposed model can be responsive to variations both in design choices and in loading conditions. The proposed model consists of two modules: physical-principle-based module and neural network (NN)-based module in which information flow from design space to response space is formulated in one complete model. Moreover, owing to robust learning capability of a new NN-based module, the model can also learn complex dynamic evolutions in response space under earthquake loading conditions, such as yielding, post-buckling and tearing, etc. Performance of the proposed model has been demonstrated with synthetic and experimental data of two connection types: extended-end-plate and top- and seat-angle with double-web-angle connection. Furthermore, the design-variable-based model can be customized to any structural component beyond the application to beam-column connections.
All Science Journal Classification (ASJC) codes
- Geotechnical Engineering and Engineering Geology
- Earth and Planetary Sciences (miscellaneous)