(untitled) (REC 2026 - 11th International Workshop on Reliable Engineering Computing: Reliability Computations in a Data and Model-Driven World, 2026)
In engineering reliability assessment, simultaneously considering hybrid uncertainty is crucial for ensuring structural safety and cost efficiency. Accurately characterizing the distribution of the conditional failure probability (CFP) is therefore of great practical importance. However, when the epistemic uncertainty involves high dimensional parameters, obtaining this distribution remains a significant challenge. To address this issue, this study proposes an efficient and accurate approach that integrates simulation-based post-processing with Bayesian inference to construct the CFP distribution. Parameter samples are first generated using a good lattice point method with partially stratified sampling, followed by reliability analysis to obtain CFP realizations. Beta mixture distribution model is then employed to directly fit the CFP, with Bayesian estimation used to determine the shape parameters of the mixture. The efficiency and accuracy of the proposed approach are demonstrated through two numerical examples, covering bimodal distributions and finite-element frame structures.