Shear Links Database – Hysteretic Performance from Numerical Simulations

This dataset presents the hysteretic performance of shear links under cyclic loading, obtained through high-fidelity numerical simulations using ANSYS Workbench 2023 R1. The research hypothesis posits that geometric parameters and material nonlinearities significantly influence the cyclic performance of shear links, which are critical components in earthquake-resistant structures. The numerical results provide a comprehensive assessment of the strength, buckling, and shear response of shear links, offering insights into their cyclic behavior. The dataset includes geometric properties, material characteristics, and key response parameters such as yield shear force, plastic shear capacity, stiffness, and shear rotation-dependent shear forces at different rotation angles. The numerical simulations employed a quasi-static, deformation-controlled cyclic loading protocol following AISC 341-10 requirements. Material and geometric nonlinearities were fully incorporated in the analysis to ensure realistic representation of shear link behavior under cyclic demands. A Chaboche nonlinear kinematic hardening model was utilized to capture the cyclic material response accurately. All simulations were performed using the finite element method with SHELL181 elements, ensuring accurate representation of plate out-of-plane deformation. The finite element mesh was refined to include 12 web height divisions for all specimens, providing sufficient resolution to capture buckling and yielding patterns. The results confirm that shear link performance is highly dependent on web slenderness, flange dimensions, and stiffener spacing. Yielding predominantly occurs in the web, with flange restraint influencing stability and delaying buckling. This dataset supports structural engineers and researchers in developing new shear link configurations and refining seismic design provisions. The numerical results can serve as input for machine-learning applications, parametric studies, or comparative analyses with experimental data. Each column in the dataset is clearly labeled, with a metadata file providing detailed descriptions to facilitate data interpretation and application.