Enriching stochastic model updating metrics : An efficient Bayesian approach using Bray-Curtis distance and an adaptive binning algorithm

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Wenhua Zhao
  • Lechang Yang
  • Chao Dang
  • Roberto Rocchetta
  • Marcos Valdebenito
  • And 1 others
  • David Moens

Detail(s)

Original languageEnglish
Article number108889
Journal / PublicationMechanical Systems and Signal Processing
Volume171
Online published3 Feb 2022
Publication statusPublished - 15 May 2022
Externally publishedYes

Abstract

In practical engineering, experimental data is not fully in line with the true system response due to various uncertain factors, e.g., parameter imprecision, model uncertainty, and measurement errors. In the presence of mixed sources of aleatory and epistemic uncertainty, stochastic model updating is a powerful tool for model validation and parameter calibration. This paper investigates the use of Bray-Curtis (B-C) distance in stochastic model updating and proposes a Bayesian approach addressing a scenario where the dataset contains multiple outliers. In the proposed method, a B-C distance-based uncertainty quantification metric is employed, that rewards models for which the discrepancy between observations and simulated samples is small while penalizing those which exhibit large differences. To improve the computational efficiency, an adaptive binning algorithm is developed and embedded into the Bayesian approximate computation framework. The merit of this algorithm is that the number of bins is automatically selected according to the difference between the experimental data and the simulated data. The effectiveness and efficiency of the proposed method is verified via two numerical cases and an engineering case from the NASA 2020 UQ challenge. Both static and dynamic cases with explicit and implicit propagation models are considered.

Research Area(s)

  • Adaptive binning algorithm, Approximate Bayesian computation, Bayesian inversion, Bray-Curtis distance, Stochastic model updating

Citation Format(s)

Enriching stochastic model updating metrics : An efficient Bayesian approach using Bray-Curtis distance and an adaptive binning algorithm. / Zhao, Wenhua; Yang, Lechang; Dang, Chao; Rocchetta, Roberto; Valdebenito, Marcos; Moens, David.

In: Mechanical Systems and Signal Processing, Vol. 171, 108889, 15.05.2022.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review