Introduction
Traditional model-based structural modal analysis and damage identification methods are typically parametric and user involved; as such, they are usually associated with demanding computational resources and require quite a lot of prior knowledge of structures. For practical applications, it would be useful to seek efficient structural identification methods that may be able to extract the salient information directly from the measured structural signals. The recently widely deployed advanced structural health monitoring (SHM) systems in structures with dense sensors also support such an effort: the massive recorded data especially call for efficient data-driven algorithms (Yang and Nagarajaiah 2014c, d) for further structural assessment.
Recently, blind source separation (BSS) has emerged as a new unsupervised machine learning tool...
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Nagarajaiah, S., Yang, Y. (2021). Blind Identification of Output-Only Systems and Structural Damage via Sparse Representations. In: Beer, M., Kougioumtzoglou, I., Patelli, E., Au, IK. (eds) Encyclopedia of Earthquake Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36197-5_77-1
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DOI: https://doi.org/10.1007/978-3-642-36197-5_77-1
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