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Time-frequency analysis of Transitory/Permanent frequency decrease in civil engineering structures during earthquakes

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 Added by Clotaire Michel
 Publication date 2008
  fields Physics
and research's language is English




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The analysis of strong motion recordings in structures is crucial to understand the damaging process during earthquakes. A very precise time-frequency representation, the reassigned smoothed pseudo-Wigner-Ville method, allowed us to follow the variation of the Millikan Library (California) and the Grenoble City Hall building (France) resonance frequencies during earthquakes. Under strong motions, a quick frequency drop, attributed to damage of the soil-structure system, followed by a slower increase is found. However, in the case of weak earthquakes, we show that frequency variations come from the ground motion spectrum and cannot be interpreted in terms of change of the soil-structure system.



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We show that the time frequency analysis of the autocorrelation function is, in many ways, a more appropriate tool to resolve fractional revivals of a wave packet than the usual time domain analysis. This advantage is crucial in reconstructing the initial state of the wave packet when its coherent structure is short-lived and decays before it is fully revived. Our calculations are based on the model example of fractional revivals in a Rydberg wave packet of circular states. We end by providing an analytical investigation which fully agrees with our numerical observations on the utility of time-frequency analysis in the study of wave packet fractional revivals.
397 - Ziyu Chen , Hau-Tieng Wu 2020
To handle time series with complicated oscillatory structure, we propose a novel time-frequency (TF) analysis tool that fuses the short time Fourier transform (STFT) and periodic transform (PT). Since many time series oscillate with time-varying frequency, amplitude and non-sinusoidal oscillatory pattern, a direct application of PT or STFT might not be suitable. However, we show that by combining them in a proper way, we obtain a powerful TF analysis tool. We first combine the Ramanujan sums and $l_1$ penalization to implement the PT. We call the algorithm Ramanujan PT (RPT). The RPT is of its own interest for other applications, like analyzing short signal composed of components with integer periods, but that is not the focus of this paper. Second, the RPT is applied to modify the STFT and generate a novel TF representation of the complicated time series that faithfully reflect the instantaneous frequency information of each oscillatory components. We coin the proposed TF analysis the Ramanujan de-shape (RDS) and vectorized RDS (vRDS). In addition to showing some preliminary analysis results on complicated biomedical signals, we provide theoretical analysis about RPT. Specifically, we show that the RPT is robust to three commonly encountered noises, including envelop fluctuation, jitter and additive noise.
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