The Bhattacharyya distance: Enriching the P-box in stochastic sensitivity analysis. (15th August 2019)

• Record Type: Journal Article
• Authors: Bi, Sifeng; Broggi, Matteo; Wei, Pengfei; Beer, Michael
• Is Part Of: Mechanical systems and signal processing. Volume 129(2019)
• Page Start: 265
• Page End: 281
• Publication Date: 2019-08-15
• Subjects: Sensitivity analysis -- Uncertainty quantification -- Uncertainty propagation -- Bhattacharyya distance -- Probability box; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2019.04.035
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Bhattacharyya distance is proposed as a novel uncertainty quantification metric. Bhattacharyya distance is a quantitative measure of the P-box in the definition of sensitivity indices. A clear logic is provided to differentiate the effect of aleatory and epistemic uncertainties. This work promotes the application of Bhattacharyya distance in uncertainty treatment. Abstract: The tendency of uncertainty analysis has promoted the transformation of sensitivity analysis from the deterministic sense to the stochastic sense. This work proposes a stochastic sensitivity analysis framework using the Bhattacharyya distance as a novel uncertainty quantification metric. The Bhattacharyya distance is utilised to provide a quantitative description of the P-box in a two-level procedure for both aleatory and epistemic uncertainties. In the first level, the aleatory uncertainty is quantified by a Monte Carlo process within the probability space of the cumulative distribution function. For each sample of the Monte Carlo simulation, the second level is performed to propagate the epistemic uncertainty by solving an optimisation problem. Subsequently, three sensitivity indices are defined based on the Bhattacharyya distance, making it possible to rank the significance of the parameters according to the reduction and dispersion of the uncertainty space of the system outputs. A tutorial case study is provided in the first part of the example to give a clear understanding of the principle of the approach with reproducible results. The second case study is the NASA Langley challenge problem, which demonstrates the feasibility of the proposed approach, as well as the Bhattacharyya distance metric, in solving such a large-scale, strong-nonlinear, and complex problem.