English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

The focus in this paper is Bayesian system identification based on noisyincomplete modal data where we can impose spatially-sparse stiffness changeswhen updating a structural model. To this end, based on a similar hierarchicalsparse Bayesian learning model from our previous work, we propose two Gibbssampling algorithms. The algorithms differ in their strategies to deal with theposterior uncertainty of the equation-error precision parameter, but bothsample from the conditional posterior probability density functions (PDFs) forthe structural stiffness parameters and system modal parameters. The effectivedimension for the Gibbs sampling is low because iterative sampling is done fromonly three conditional posterior PDFs that correspond to three parametergroups, along with sampling of the equation-error precision parameter fromanother conditional posterior PDF in one of the algorithms where it is notintegrated out as a "nuisance" parameter. A nice feature from a computationalperspective is that it is not necessary to solve a nonlinear eigenvalue problemof a structural model. The effectiveness and robustness of the proposedalgorithms are illustrated by applying them to the IASE-ASCE Phase II simulatedand experimental benchmark studies. The goal is to use incomplete modal dataidentified before and after possible damage to detect and assessspatially-sparse stiffness reductions induced by any damage. Our past andcurrent focus on meeting challenges arising from Bayesian inference ofstructural stiffness serve to strengthen the capability of vibration-basedstructural system identification but our methods also have much broaderapplicability for inverse problems in science and technology where systemmatrices are to be inferred from noisy partial information about theireigenquantities.

Bayesian System Identification based on Hierarchical Sparse Bayesian Learning and Gibbs Sampling with Application to Structural Damage Assessment