Model selection and parameter estimation in structural dynamics using approximate Bayesian computation. (15th January 2018)

• Record Type: Journal Article
• Authors: Ben Abdessalem, Anis; Dervilis, Nikolaos; Wagg, David; Worden, Keith
• Is Part Of: Mechanical systems and signal processing. Volume 99(2017)
• Page Start: 306
• Page End: 325
• Publication Date: 2018-01-15
• Subjects: Model selection -- Parameter estimation -- Approximate Bayesian computation -- Sequential Monte Carlo -- Nonlinearity; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2017.06.017
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Automatic model selection and parameter estimation based on different features. Parameter estimation. Introduction for first time in structural dynamics of the Approximate Bayesian computation for model selection. Sequential Monte Carlo. Nonlinearity. Abstract: This paper will introduce the use of the approximate Bayesian computation (ABC) algorithm for model selection and parameter estimation in structural dynamics. ABC is a likelihood-free method typically used when the likelihood function is either intractable or cannot be approached in a closed form. To circumvent the evaluation of the likelihood function, simulation from a forward model is at the core of the ABC algorithm. The algorithm offers the possibility to use different metrics and summary statistics representative of the data to carry out Bayesian inference. The efficacy of the algorithm in structural dynamics is demonstrated through three different illustrative examples of nonlinear system identification: cubic and cubic-quintic models, the Bouc-Wen model and the Duffing oscillator. The obtained results suggest that ABC is a promising alternative to deal with model selection and parameter estimation issues, specifically for systems with complex behaviours.