• Identification of load transfer mechanism of dry beam–column connection. • Development of collapse-resisting design of high-performance dry connection. • Development of macro joint model considering joint load transfer mechanisms. • Modification of reliability assessment method for strongly nonlinear problem. • Gaussian process regression model-based reliability sensitivity analysis. Beam–column joint connections are critical for precast concrete frames to resist collapse, yet their internal load transfer mechanisms and demand-side uncertainties remain underexplored. This study develops a preliminary reliability-aware collapse-resisting design framework for a high-performance dry connection, incorporating strengthened steel angles and high-strength bolts. The design part begins with the identification of load transfer mechanisms and configuration optimization to eliminate undesirable failure modes based on detailed finite element models. Thereby the design focuses on upper-bound estimations of column bolt axial force demand, derived based on rebar ultimate strengths. The reliability assessment of the derived demand relies on: (1) a newly developed and validated macro joint model, alleviating the computational burden of repeated model evaluations; and (2) a modified Active Learning Probabilistic Integration method ensuring highly efficient reliability assessment using a small number of model evaluations. The learning function and the point selection strategy are tailored to the highly nonlinear nature of the involved performance function. Material uncertainties in the structural components connected by the joint are explicitly considered because they significantly influence joint demands. Moreover, variance-based global sensitivity analysis and local reliability sensitivity analysis are performed by post-processing the Gaussian Process regression model obtained from the reliability assessment. The results indicate that the rebar ultimate strength, yield strength, and fracture strain are the most influential random variables affecting the design reliability. Their mean values are recommended to be explicitly considered in the design phase in future research; tighter quality control on rebar production, aimed at reducing material property variability, can further improve the design reliability.
Zhao et al. (Sun,) studied this question.