Journal
STRUCTURAL HEALTH MONITORING: RESEARCH AND APPLICATIONS
Volume 558, Issue -, Pages 52-+Publisher
TRANS TECH PUBLICATIONS LTD
DOI: 10.4028/www.scientific.net/KEM.558.52
Keywords
Recursive stochastic subspace identification; auto-associate neural network; structural health monitoring
Funding
- National Science Council, Taiwan [NSC 98-2625-M-002-018-MY3]
- Research Program of Excellency of National Taiwan University [99R80805]
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This paper deals with the problem of a bridge structure identification using output-only vibration measurements under changing environmental conditions. Two key issues of a real-life monitoring system are addressed through analysis. The first issue is the identification of structural dynamic characteristics directly from measurements under operating conditions. The covariance-driven recursive stochastic subspace identification (RSSI-COV) algorithm is applied to extract the system dynamic characteristics. The second issue is to distinguish the system dynamic features caused by abnormality from those caused by environmental and operational variations, such as temperature, and traffic loading. In this study a solution is proposed to model and remove the uncertainty due to environmental effects from the identified system dynamic characteristics from on-going measurements. Nonlinear principal component analysis incorporated with AANN is employed to distinguish the dynamic feature changes caused by abnormality from those caused by environmental and operational variation (i.e. ambient temperature and traffic loadings). Finally, field experiment of a bridge is conducted. The variation of the identified system natural frequencies was discussed by using the proposed method.
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