Damage identification in civil engineering structures utilizing PCA-compressed residual frequency response functions and neural network ensembles

Jianchun Li, Ulrike Dackermann, You Lin Xu, Bijan Samali

Research output: Journal article publicationJournal articleAcademic researchpeer-review

71 Citations (Scopus)

Abstract

This paper presents a non-destructive, global, vibration-based damage identification method that utilizes damage pattern changes in frequency response functions (FRFs) and artificial neural networks (ANNs) to identify defects. To extract damage features and to obtain suitable input parameters for ANNs, principal component analysis (PCA) techniques are applied. Residual FRFs, which are the differences in the FRF data from the intact and the damaged structure, are compressed to a few principal components and fed to ANNs to estimate the locations and severities of structural damage. A hierarchy of neural network ensembles is created to take advantage of individual information from sensor signals. To simulate field-testing conditions, white Gaussian noise is added to the numerical data and a noise sensitivity study is conducted to investigate the robustness of the developed damage detection technique to noise. Both numerical and experimental results of simply supported steel beam structures have been used to demonstrate effectiveness and reliability of the proposed method.
Original languageEnglish
Pages (from-to)207-226
Number of pages20
JournalStructural Control and Health Monitoring
Volume18
Issue number2
DOIs
Publication statusPublished - 1 Mar 2011

Keywords

  • artificial neural network
  • damage identification
  • frequency response functions
  • neural network ensemble
  • principal component analysis
  • structural health monitoring

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Mechanics of Materials

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