Novel tensor subspace system identification algorithm to identify time-varying modal parameters of bridge structures. (July 2022)

• Record Type: Journal Article
• Authors: Zhang, Erhua; Wu, Di; Shan, Deshan
• Is Part Of: Structural health monitoring. Volume 21:Number 4(2022)
• Page Start: 1541
• Page End: 1554
• Publication Date: 2022-07
• Subjects: subspace identification -- tensor decomposition -- time-varying system -- output-only identification -- bridge structures; Structural health monitoring -- Periodicals; Structural stability -- Periodicals; Strength of materials -- Periodicals; Nondestructive testing -- Periodicals; Constructions -- Stabilité -- Périodiques; Résistance des matériaux -- Périodiques; Contrôle non destructif -- Périodiques; Electronic journals; 624.17
• DOI: 10.1177/14759217211036024
• Languages: English
• ISSNs: 1475-9217

Abstract

Subspace-based system identification algorithms have been developed as an advanced technique for performing modal analysis. We introduce a novel tensor subspace-based algorithm to identify the time-varying modal parameters of bridge structures. A new time dimension is introduced in the traditional Hankel matrix, and a mathematical model of tensor subspace decomposition is established. Combined with the stabilization diagram, tensor parallel factor decomposition is used to estimate the frequencies, mode shapes, and modal damping ratios. The effectiveness of the proposed algorithm is validated by comparing it with the classical sliding-window–based stochastic subspace algorithm on a model cable-stayed bridge dynamic test. The proposed algorithm is further applied to process the dynamic responses of a real bridge health monitoring system to identify its time-varying modal frequencies. Our results demonstrated that the proposed algorithm significantly reduces computational efforts and extends the range of solution ideas for future out-only time-varying system identification problems.