Spectral identification of nonlinear multi-degree-of-freedom structural systems with fractional derivative terms based on incomplete non-stationary data

• Published in: Structural safety Vol. 86; p. 101975
• Main Authors: dos Santos, Ketson R.M., Brudastova, Olga, Kougioumtzoglou, Ioannis A.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.09.2020; Elsevier BV
• Subjects: Compressive sampling; Degrees of freedom; Fractional derivative; Free surfaces; Frequency dependence; Frequency response functions; Generalized harmonic wavelet; Incomplete data; Nonlinear systems; Parameter identification; Sea states; System identification; Time dependence; Generalized harmonic wavelet; Incomplete data; System identification; Nonlinear systems; Compressive sampling; Fractional derivative
• DOI: 10.1016/j.strusafe.2020.101975
• ISSN: 0167-4730

•A spectral identification technique is developed for multi-degree-of-freedom systems.•The technique can address nonlinear systems with fractional derivative terms.•The technique can account for non-stationary data via harmonic wavelets.•The technique can account for incomplete data via a compressive sampling treatment. A novel spectral identification technique is developed for determining the parameters of nonlinear and time-variant multi-degree-of-freedom (MDOF) structural systems based on available input-output (excitation-response) realizations. A significant advantage of the technique relates to the fact that it can readily account for the presence of fractional derivative terms in the system governing equations, as well as for the cases of non-stationary, incomplete and/or noise corrupted data. Specifically, the technique relies on recasting the governing equations as a set of multiple-input/multiple-output systems in the wavelet domain. Next, an l1-norm minimization procedure based on compressive sampling theory is employed for determining the wavelet coefficients of the available incomplete non-stationary input-output data. Finally, these wavelet coefficients are utilized to reconstruct the non-stationary incomplete signals, and consequently, to determine system related time- and frequency-dependent wavelet-based frequency response functions and associated parameters. Two illustrative MDOF systems are considered in the numerical examples for demonstrating the reliability of technique. The first refers to a nonlinear time-variant system with fractional derivative terms, while the second addresses a nonlinear offshore structural system subjected to flow-induced forces. It is worth noting that for the offshore system, a novel recently proposed evolutionary version of the widely used JONSWAP spectrum is employed for modeling the non-stationary free-surface elevation in cases of time-dependent sea states.