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- W4384208064 abstract "The power spectral density function of signals measured from structures subjected to external loads contains a significant amount of information about the modal parameters (natural frequencies, damping ratios). System identification techniques can be illustrated as techniques for extracting modal parameters from these signals or their spectra. Another technique to extract parameters is to estimate the parameters(mean, variance) of a probability distribution using sampling(signals). In this study, it is proposed a technique to estimate the modal parameters by replacing the power spectral density function with a probability density function and using the existing parameter extraction technique (maximum likelihood method) in the field of probability statistics. A technique is developed to estimate the mode shape and to sequentially calculate the natural frequency and damping ratio from the mode response decomposed by the mode shape. Furthermore, if the external load can be replaced by a spectral model, the load model is integrated in the modal identification framework. Numerical analysis and application to the response of a real structure are performed to verify the proposed method in this study, and the verification results show that the proposed mode identification framework is stable and is able to estimate modal parameters with high reliability." @default.
- W4384208064 created "2023-07-14" @default.
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- W4384208064 date "2023-06-30" @default.
- W4384208064 modified "2023-09-24" @default.
- W4384208064 title "A Concurrent Modal-Load Identification framework Based on the Probability Analogy of Power Spectral Density Function" @default.
- W4384208064 doi "https://doi.org/10.37109/weik.2023.27.2.57" @default.
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