Estimation of full-field, full-order experimental modal model of cable vibration from digital video measurements with physics-guided unsupervised machine learning and computer vision

Yang, Yongchao; Sanchez, Lorenzo; Zhang, Huiying; Roeder, Alexander Aryn; Bowlan, John M.; Crochet, Jared John; Farrar, Charles Reed; Mascarenas, David Dennis Lee

doi:10.1002/stc.2358

Title: Estimation of full-field, full-order experimental modal model of cable vibration from digital video measurements with physics-guided unsupervised machine learning and computer vision

Journal Article · Mon Apr 01 00:00:00 EDT 2019 · Structural Control and Health Monitoring

DOI:https://doi.org/10.1002/stc.2358· OSTI ID:1680014

^[1]; Sanchez, Lorenzo ^[2]; Zhang, Huiying ^[3]; Roeder, Alexander Aryn ^[4];

^[1];

^[1]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Brown Univ., Providence, RI (United States)
Columbia Univ., New York, NY (United States)
Georgia Inst. of Technology, Atlanta, GA (United States)

Cables are critical components for a variety of structures such as stay cables and suspenders of cable-stayed bridges and suspension bridges. When in operational service, they are vulnerable to cumulative fatigue damage induced by dynamic loads (e.g., the cyclic vehicle loads and wind excitation). To accurately analyze and predict their dynamics behaviors and performance that could be spatially local and temporal transient, it is essential to perform high-resolution vibration measurements, from which their dynamics properties are identified and, subsequently, a high spatial resolution, full-modal-order dynamics model of cable vibration can be established. In this study, we develop a physics-guided, unsupervised machine learning-based video processing approach that can blindly and efficiently extract the full-field (as many points as the pixel number of the video frame) modal parameters of cable vibration using only the video of an operating (output-only) cable. In particular, by incorporating the physics of cable vibration (taut string model), a novel automated modal motion filtering method is proposed to enable autonomous identification of full-order (as many modes as possible) dynamic parameters, including those weakly excited modes that used to be challenging to identify in operational modal analysis. Therefore, a full-field, full-order modal model of cable vibration is established by the proposed method. Furthermore, this new approach provides a low-cost and noncontact technique to estimate the cable tension using only the video of the vibrating cable where the fundamental frequency is automatically and efficiently estimated to compute the cable tension according to the taut string equation. Laboratory experiments on a bench-scale cable are conducted to validate the developed approach.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: 89233218CNA000001; 20150708PRD2

OSTI ID:: 1680014

Alternate ID(s):: OSTI ID: 1504296

Report Number(s):: LA-UR-16-28051

Journal Information:: Structural Control and Health Monitoring, Vol. 26, Issue 6; ISSN 1545-2255

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

Citation Metrics:

Cited by: 24 works

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