toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
  Record Links
Author (up) Dang, C.; Valdebenito, M.A.; Faes, M.G.R.; Wei, P.F.; Beer, M. doi  openurl
  Title Structural reliability analysis: A Bayesian perspective Type
  Year 2022 Publication Structural Safety Abbreviated Journal Struct. Saf.  
  Volume 99 Issue Pages 102259  
  Keywords Failure probability; Bayesian inference; Gaussian process; Numerical uncertainty; Parallel computing  
  Abstract Numerical methods play a dominant role in structural reliability analysis, and the goal has long been to produce a failure probability estimate with a desired level of accuracy using a minimum number of performance function evaluations. In the present study, we attempt to offer a Bayesian perspective on the failure probability integral estimation, as opposed to the classical frequentist perspective. For this purpose, a principled Bayesian Failure Probability Inference (BFPI) framework is first developed, which allows to quantify, propagate and reduce numerical uncertainty behind the failure probability due to discretization error. Especially, the posterior variance of the failure probability is derived in a semi-analytical form, and the Gaussianity of the posterior failure probability distribution is investigated numerically. Then, a Parallel Adaptive-Bayesian Failure Probability Learning (PA-BFPL) method is proposed within the Bayesian framework. In the PA-BFPL method, a variance-amplified importance sampling technique is presented to evaluate the posterior mean and variance of the failure probability, and an adaptive parallel active learning strategy is proposed to identify multiple updating points at each iteration. Thus, a novel advantage of PA-BFPL is that both prior knowledge and parallel computing can be used to make inference about the failure probability. Four numerical examples are investigated, indicating the potential benefits by advocating a Bayesian approach to failure probability estimation.  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0167-4730 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000837863500001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1637  
Permanent link to this record
Select All    Deselect All
 |   | 

Save Citations:
Export Records: