On the use of the cepstrum and artificial neural networks to identify structural mass changes from response-only measurements

Publisher:
KU Leuven - Departement Werktuigkunde
Publication Type:
Conference Proceeding
Citation:
Published on CD-ROm, 2014, pp. 3739 - 3750
Issue Date:
2014
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This paper presents a damage identification technique based on response-only data utilising cepstrum analysis and artificial neural networks (ANNs) for the identification of added mass in a two-storey framed structure. The proposed technique applies cepstrum-based operational modal analysis (OMA) for the regeneration of frequency response functions (FRFs), and added mass is detected through the combined use of principal component analysis (PCA) for data compression and ANNs for feature extraction and pattern recognition. In particular, different treatments of the zeros in the curve-fitting of the transfer function cepstrum are investigated to improve the automation potential of the method for application in continuous online structural health monitoring (SHM). The proposed technique is validated on a laboratory structure tested on a large-scale shake table with ambient base loading. The results of the investigation show that the method is effective in identifying added mass based on response-only measurements.
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