Structural reliability analysis with parametric p-box uncertainties via a Bayesian updating BDRM.

The parametric probability-box model, often abbreviated as "p-box" is frequently used to characterize epistemic uncertainties. However, structural reliability analysis with p-box uncertainties can often be computationally intensive. This paper presents an efficient method to accurately compute the bounds of failure probabilities within this context. The method's key innovation lies in its ability to achieve high efficiency with only a single round of model evaluations. First, the Fractional Exponential Moments-based Maximum Entropy Method (FEM-MEM) with Bivariate Dimension Reduction Method (BDRM) is employed for precise reliability assessment, where a single-round of model evaluations are carried out. Subsequently, Bayesian Updating (BU) is applied to adjust the weights obtained from BDRM in response to changes in input variables, while ensuring the invariance of integration points. Following this adjustment, the FEM-MEM is once again employed to compute the failure probability after the information change, without necessitating additional model evaluations. To compute the bounds of failure probabilities, a Kriging model is employed to construct a surrogate relationship between the interval variables and failure probabilities. The accuracy and efficiency of the proposed method are demonstrated through numerical examples, with comparisons made against pertinent double-loop method. • The weights of BDRM are updated via Bayesian updating with the information change. • The proposed method conduct reliability assessment with only single round of LSF evaluations. • The Kriging model provides accurate upper and lower bounds of reliability index with minimal training data. • The proposed method allows for the robust and accurate results involving p-box uncertainties. [ABSTRACT FROM AUTHOR]