Structural damage detection under unknown environmental conditions using Sparse Bayesian factor analysis

Yong Xia, Xiaoyou Wang, Xiaoqing Zhou

Research output: Journal article publicationConference articleAcademic researchpeer-review

Abstract

This study develops a novel structural damage detection method using the factor analysis in the sparse Bayesian learning framework. The unknown changing environmental factors that affect structural vibration properties are treated as latent variables. Structural frequencies that are measured within a long period are utilized as the observations in the factor analysis (FA) model. The ARD model is defined on the factor loading matrix for automatic dimension selection. Using the iterative expectation-maximization technique, the number of underlying environmental factors is determined automatically. With the optimized parameters and variables, the measurement data are reconstructed. The reconstruction error is defined as the damage indicator, which will get large with the emergence of damage. One advantage of the proposed Bayesian FA model is that the assumption of independence of vibration modes is not required, as the covariance matrix of the residue vector in the FA model is treated as a full matrix rather than a diagonal matrix. Two laboratory-tested examples are utilized to verify the effectiveness of the proposed method. Results indicate that structural damage can be accurately detected under changing environmental conditions.

Original languageEnglish
Pages (from-to)553-555
Number of pages3
JournalInternational Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII
Volume2021-June
Publication statusPublished - Jul 2021
Event10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2021 - Porto, Portugal
Duration: 30 Jun 20212 Jul 2021

Keywords

  • Damage detection
  • Environmental variations
  • Factor analysis
  • Sparse Bayesian learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems and Management
  • Civil and Structural Engineering
  • Building and Construction

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