Abstract
Long-term and often short-term vibration-based structural health monitoring data show variations in response even though there is no visible change in structural properties. Although these variations are attributed to environmental factors causing change in structural parameters, such uncertainties in structural parameters make the condition assessment of a structure difficult. In this study, the autoregressive (AR) model, where the coefficients of the model are related to structural model parameters and are considered as one of the efficient tools often used in modal identification and damage detection, is used to investigate its efficiency in damage detection and localization when parametric uncertainties are present. A numerical study conducted with an eight-story shear building, where uncertainties in stiffness are assumed in terms of known probability density functions, shows that the AR model is highly efficient in damage detection and localization even when significant parametric uncertainties are present. For this purpose, damage is being induced in a particular story, and the response is analyzed with the autoregressive model to gauge the efficiency of the model. To broaden the practical applicability of the method when noise is present in the measurement data, the Kalman filter approach has been adopted and successfully shown to handle the noisy data.
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Roy, K., Ray-Chaudhuri, S. (2013). Autogressive Model for Structural Condition Assessment in Presence of Parametric Uncertainty. In: Chakraborty, S., Bhattacharya, G. (eds) Proceedings of the International Symposium on Engineering under Uncertainty: Safety Assessment and Management (ISEUSAM - 2012). Springer, India. https://doi.org/10.1007/978-81-322-0757-3_73
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DOI: https://doi.org/10.1007/978-81-322-0757-3_73
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