Removal of masking effect for damage detection of structures

William Soo Lon Wah, John S. Owen, Yung Tsang Chen, Ahmed Elamin, Gethin Wyn Roberts

Research output: Journal PublicationArticlepeer-review

18 Citations (Scopus)

Abstract

Damage detection of civil engineering structures relies heavily on the use of outlier analysis/novelty detection analysis. Generally, data captured from a structure in its normal environmental condition are used to create a model and compute control limits to represent the normal range of variations of damage sensitive features of the structure. However, the training database used usually includes outlier measurements, which may introduce masking effect. These outlier measurements can affect the mean and standard deviation/covariance matrix of the training database, and hence, affect the model and the control limits. As a result, small damage may not be detected. Therefore, this paper proposes an approach of selecting a ‘clean’ training database for the construction of the baseline of the undamaged structure so as to detect damage at an earlier stage. The approach makes use of Principal Component Analysis and Median Absolute Deviation to identify outlier measurements. This approach can be applied before the application of damage detection methods to allow damage to be detected at an earlier stage. The proposed approach is applied to a numerical beam model and the Z24 Bridge, in Switzerland. The results obtained demonstrate that damage can be detected at an earlier stage using the approach proposed in this paper. The proposed method also allows the determination of the model (e.g. linear or nonlinear) to be used for damage detection.

Original languageEnglish
Pages (from-to)646-661
Number of pages16
JournalEngineering Structures
Volume183
DOIs
Publication statusPublished - 15 Mar 2019

Keywords

  • Damage detection
  • Environmental and operational conditions
  • Gaussian Mixture Model
  • Masking effect
  • Median Absolute Deviation
  • Novelty detection analysis
  • Outlier analysis
  • Principal Component Analysis
  • Regression analysis

ASJC Scopus subject areas

  • Civil and Structural Engineering

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