Browsing by Author "Zhang, Hanshu"

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    A NOVEL METHOD FOR ESTIMATING THE EVOLUTIONARY SPECTRA OF NONSTATIONARY RANDOM PROCESSES
    (2022-12-01) Zhang, Hanshu; Spanos, Pol
    Evolutionary spectra, which capture the frequency profile change in time domain, is the key quantity in vibrations. An approach for estimating the evolutionary power and cross spectrum of nonstationary stochastic processes is developed through the determination of the total energy of lightly damped linear systems. In this regard, a novel approach is discussed for appropriately smoothing the total energy of a linear single degree of freedom (SDOF) system, based on the use of Savitzky-Golay moving average filter. Further, a novel approximated energy function is proposed to fit the numerical total energy of the SDOF system. Then, the evolutionary power and cross spectrum can be generated by the approximated energy function. In this thesis, several numerical applications have been used to test the accuracy and reliability of the proposed energy function. In this regard, uni- and multi-variate nonstationary stochastic processes are simulated through the autoregressive (AR) algorithm and spectral representation method. Then, the evolutionary power and cross spectrum of simulated time series are estimated and compared to the target evolutionary power and cross spectrum to assess the reliability of the proposed method. Furthermore, real earthquake data are used to estimate the corresponding evolutionary spectra by the proposed approach, and accuracy of the results is proved by comparing with the evolutionary spectra obtained by generalized harmonic wavelets (GHWs) transform.
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    Bivariate Processes Evolutionary Power Spectral Density Estimation Using Energy Spectrum Equations
    (IOP Publishing, 2024) Spanos, Pol D.; Matteo, Alberto Di; Zhang, Hanshu; Yue, Qingxia
    In this paper a novel procedure is developed for evolutionary cross power spectra (ECPS) estimation of bivariate nonstationary stochastic processes. Specifically, the ECPS is determined by estimating the statistical moments of energy-like response quantities of lightly damped linear filters excited by nonstationary stochastic processes. In this context, a smoothing procedure is incorporated by using the Savitzky-Golay (S-G) moving average filter to obtain reliable ECPS based even from a limited number of available records. Further, a refinement of the approach is proposed relying on polynomial based functions of the system output. Several numerical examples, including nonstationary processes with known spectra, and historic accelerograms are used to assess the reliability and accuracy of the proposed procedure.
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    Evolutionary power spectral density estimation using energy spectrum equation
    (IASSAR, 2021) Spanos, Pol D.; Di Matteo, Alberto; Zhang, Hanshu; Yue, Qingxia; Pirrotta, Antonina
    In this paper a novel approach is developed for the determination of evolutionary power spectra (EPS) of non-stationary random processes that can be used as models of earthquake accelerograms. Specifically, the spectra are estimated by considering the statistical moments of the energy of lightly damped linear systems (filters) excited by the stochastic seismic process. In this manner, an estimate of the EPS of the seismic input is derived by varying the frequency of the linear system. For this purpose, an appropriate smoothing procedure is also incorporated, relying on the use of the so-called Savitzky-Golay moving average filter. This is done for obtaining reliable spectra based on a relatively small number of available records. Further, a possible refinement of the approach is investigated introducing a polynomial representation for the mean energy of the filter output. Finally, several examples involving both simulated data with known target spectrum, and measured data are used to show the usefulness and reliability of the proposed approach.