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Removal of masking effect for damage detection of structures

Removal of masking effect for damage detection of structures

Soo Lon Wah, William, Owen, John S., Chen, Yung-Tsang, Elamin, Ahmed ORCID: 0000-0003-0783-5185 and Roberts, Gethin Wyn (2019) Removal of masking effect for damage detection of structures. Engineering Structures, 183. pp. 646-661. ISSN 0141-0296 (doi:https://doi.org/10.1016/j.engstruct.2019.01.005)

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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.

Item Type: Article
Uncontrolled Keywords: damage detection, outlier analysis, novelty detection analysis, masking effect, environmental and operational conditions, regression analysis, principal component analysis, median absolute deviation, gaussian mixture model
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > School of Engineering (ENN)
Last Modified: 19 Sep 2020 00:20
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: REF 1
URI: http://gala.gre.ac.uk/id/eprint/27476

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