A regional attenuation-based active learning method and its combination with minimax SSA for time-variant hybrid reliability analysis

A major challenge in structural reliability is that computational accuracy and efficiency are hard to balance, especially the time-variant hybrid reliability problem. To improve the reliability analysis efficiency under the condition that the accuracy is assured, this paper develops a new learning function named Regional Learning with Weighted Simulation (RLWS) to estimate the failure probability. The Kriging model is first employed as a response surface to fit the response of limit state function. An adaptive regional learning model as the core of the most probable point is then proposed to update the Kriging model, where the boundary of the updated region is determined by an attenuation function. Furthermore, based on the RLWS model, this paper develops a continuous maximin salp swarm algorithm to calculate the upper bound of the response value for time-variant hybrid performance function. Meanwhile, the Salp Swarm Algorithm is employed to calculate the lower bound of the response value. The Monte Carlo simulation can thus facilitate evaluation based on the final generated Kriging response surface. Several case studies are performed to test and validate the effectiveness of the proposed method and its applicability to practical engineering problems.