Enriching stochastic model updating metrics: An efficient Bayesian approach using Bray-Curtis distance and an adaptive binning algorithm. (15th May 2022)

• Record Type: Journal Article
• Authors: Zhao, Wenhua; Yang, Lechang; Dang, Chao; Rocchetta, Roberto; Valdebenito, Marcos; Moens, David
• Is Part Of: Mechanical systems and signal processing. Volume 171(2022)
• Publication Date: 2022-05-15
• Subjects: Bayesian inversion -- Stochastic model updating -- Approximate Bayesian computation -- Bray-Curtis distance -- Adaptive binning algorithm; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2022.108889
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: The Bayesian inversion for stochastic model updating is investigated. A novel UQ metric using the Bray-Curtis distance is proposed. An approximate Bayesian computation framework is developed. An adaptive binning algorithm is proposed to improve the computation efficiency. Both static and dynamic cases with explicit and implicit models are considered. 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.