State-of-the-art review on Bayesian inference in structural system identification and damage assessment

• Published in: Advances in structural engineering Vol. 22; no. 6; pp. 1329 - 1351
• Main Authors: Huang, Yong, Shao, Changsong, Wu, Biao, Beck, James L., Li, Hui
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.04.2019
• Subjects: Bayesian model updating; uncertainty quantification; Bayesian model class assessment; structural system identification; structural health monitoring; Bayesian inference; sparse Bayesian learning; damage assessment
• DOI: 10.1177/1369433218811540
• ISSN: 1369-4332

Bayesian inference provides a powerful approach to system identification and damage assessment for structures. The application of Bayesian method is motivated by the fact that inverse problems in structural engineering, including structural health monitoring, are typically ill-conditioned and ill-posed when using noisy incomplete data because of various sources of modeling uncertainties. One should not just search for a single “optimal” value for the vector of model parameters but rather attempt to describe the whole family of plausible model parameters based on measured data using a Bayesian probabilistic framework. In this article, the fundamental principles of Bayesian analysis and computation are summarized; then a review is given of recent state-of-the-art practices of Bayesian inference in system identification and damage assessment for civil infrastructure. Discussions of the benefits and deficiencies of these approaches, as well as potentially useful avenues for future studies, are also provided. Our focus is on meeting challenges that arise from system identification and damage assessment for the civil infrastructure but our presented theories also have a considerably broader applicability for inverse problems in science and technology.