Nonlinear mode decomposition-based methodology for modal parameters identification of civil structures using ambient vibrations. (21st October 2019)

• Record Type: Journal Article
• Authors: Yanez-Borjas, Jesus J; Amezquita-Sanchez, Juan P; Valtierra-Rodriguez, Martin; Camarena-Martinez, David
• Is Part Of: Measurement science & technology. Volume 31:Number 1(2020)
• Publication Date: 2019-10-21
• Subjects: modal parameters identification -- autocorrelation function -- nonlinear mode decomposition -- Hilbert spectral analysis; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ab3fe2
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: The accurate and effective identification of modal parameters such as natural frequencies and damping ratios represents a key issue for different applications in civil engineering, e.g. updating of analytical models, vibration control, and structural health monitoring. Therefore, the development and application of new methods to perform this task with high accuracy have attracted the interest of many researchers around the world. This paper presents a new methodology based on the nonlinear mode decomposition to estimate the natural frequencies and damping ratios of civil structures subjected to ambient vibrations. This type of signals represents a challenge due to that the measured structural response is embedded into noisy signals with low amplitude. In order to validate the methodology, three different examples are used: two based on analytical models and one based on real data. Results show that the proposal can estimate the natural frequencies, especially the closely spaced modes, and damping ratios of civil structures subjected to ambient vibrations with a high accuracy.