Abstract
Damage detection and system identification with output-only information is an important but challenging task for ensuring the safety and functionality of civil structures during their service life. In this paper, a relatively simple and efficient iteration identification method consisting of the least squares estimation (LSE) technique and an input modification process is proposed for the simultaneous identification of structural parameters and the unknown ground motion. The spatial distribution characteristics of ground acceleration on earthquake-excited building structures are considered as additional information for parameters identification in each iterative step. First, the unknown input is estimated using the measured responses and the initial guesses of the structural parameters. The estimated input is then modified on the basis of the property of its spatial distribution. This modified input is further employed for providing the updated estimation of structural parameters. The iterative procedure would continue until the preset convergence criterion is satisfied. The accuracy of the proposed approach is numerically validated via a shear building model under the El Centro earthquake. The effects of signal noise, the number of sample points, and the initial guesses of structural parameters are discussed. The results show that the proposed approach can satisfactorily identify the structural parameters and unknown earthquakes.
Highlights
Since the loadings applied to the structures are of importance for structural evaluation and future life prediction, the development of loading identification techniques is highly required
An iterative identification procedure based on the least squares estimation (LSE) technique and an input modification process is proposed for the simultaneous identification of the parameters of building structures and the unknown ground motion
The building structure is assumed to be subject to El-Centro earthquake with a peak ground acceleration (PGA) of 0.34 g
Summary
Many civil structures in service may deteriorate or degrade due to many factors, such as environmental corrosion, long-term fatigue, strong winds, severe earthquakes, and so forth. The failure of these deficient structures could be catastrophic, in terms of loss of life and the economy, and of subsequent social and psychological impacts [1]. It is imperative to identify the parameters of the structure, detect the damage, and guide the maintenance to ensure structural safety and functionality. Since the loadings applied to the structures are of importance for structural evaluation and future life prediction, the development of loading identification techniques is highly required
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