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dc.contributor.authorFan, X.
dc.contributor.authorLi, Jun
dc.contributor.authorHao, Hong
dc.identifier.citationFan, X. and Li, J. and Hao, H. 2016. Piezoelectric impedance based damage detection in truss bridges based on time frequency ARMA model. Smart Structures and Systems. 19 (3): pp. 501-523.

Electromechanical impedance (EMI) based structural health monitoring is performed by measuring the variation in the impedance due to the structural local damage. The impedance signals are acquired from the piezoelectric patches that are bonded on the structural surface. The impedance variation, which is directly related to the mechanical properties of the structure, indicates the presence of local structural damage. Two traditional EMI-based damage detection methods are based on calculating the difference between the measured impedance signals in the frequency domain from the baseline and the current structures. In this paper, a new structural damage detection approach by analyzing the time domain impedance responses is proposed. The measured time domain responses from the piezoelectric transducers will be used for analysis. With the use of the Time Frequency Autoregressive Moving Average (TFARMA) model, a damage index based on Singular Value Decomposition (SVD) is defined to identify the existence of the structural local damage. Experimental studies on a space steel truss bridge model in the laboratory are conducted to verify the proposed approach. Four piezoelectric transducers are attached at different locations and excited by a sweep-frequency signal. The impedance responses at different locations are analyzed with TFARMA model to investigate the effectiveness and performance of the proposed approach. The results demonstrate that the proposed approach is very sensitive and robust in detecting the bolt damage in the gusset plates of steel truss bridges.

dc.titlePiezoelectric impedance based damage detection in truss bridges based on time frequency ARMA model
dc.typeJournal Article
dcterms.source.titleSmart Structures and Systems
dcterms.source.seriesSmart Structures and Systems
curtin.departmentDepartment of Civil Engineering
curtin.accessStatusFulltext not available

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