An inverse identification strategy for the mechanical parameters of a phenomenological hysteretic constitutive model. (May 2020)

• Record Type: Journal Article
• Authors: Sessa, Salvatore; Vaiana, Nicoló; Paradiso, Massimo; Rosati, Luciano
• Is Part Of: Mechanical systems and signal processing. Volume 139(2020)
• Publication Date: 2020-05
• Subjects: Inverse identification -- Hysteretic materials -- Bouc Wen model; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2020.106622
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: We present an algorithm for the identification of experimental hysteresis loops. The algorithm uses a previously investigated and very versatile constitutive model. It proved to be robust, stable and accurate in analyzing four experimental loops. Abstract: An inverse strategy is developed for identifying the parameters of the hysteretic phenomenological constitutive model presented in Vaiana et al. (2019) and belonging to a wider class of hysteretic models. The model, differently from the celebrated Bouc-Wen one, permits the definition of either stress-strain or load-displacement relationships by closed-form expressions that do not require any iterative algorithm for the complete characterization of its response. The identification strategy is based on two optimization procedures performed in sequence in which a mean-square residual between a target and a computed response is minimized. The computation of suitable first trials is shown to represent an essential step of the procedure and is performed by taking advantage of the fact that its parameters correspond to physical quantities characterizing the experimental hysteretic loop. The procedure has been tested by identifying the mechanical parameters of two theoretical and four experimental responses for which numerical results prove the robustness and effectiveness of the proposed identification strategy.