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A baseline-free damage detection method using VBI incomplete measurement data

Mousavi, M, Holloway, D ORCID: 0000-0001-9537-2744, Olivier, JC ORCID: 0000-0002-7703-6357 and Gandomi, AH 2021 , 'A baseline-free damage detection method using VBI incomplete measurement data' , Measurement, vol. 174 , pp. 1-16 , doi: 10.1016/j.measurement.2020.108957.

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A novel baseline-free method for damage detection of vehicle-bridge interaction (VBI) systems is proposed. The proposed method is physics-based, in contrast to many prevailing approaches, which are purely data-based techniques. It uses incomplete measurement data by incorporating the static condensation transformation matrix into the equations to obtain the final formulas. However, the static condensation of the damaged beam is not known a priori. Therefore, it is shown analytically that the static condensation transformation matrix of the undamaged beam can be used instead of the one corresponding to the damaged beam. This has been confirmed through numerical simulations for different boundary conditions of the beam. Various factors are studied numerically in order to demonstrate the robustness of the proposed method, including road roughness, boundary conditions, variable moving mass velocity and measurement noise. The results demonstrate the capability of the proposed method in damage detection of beam-type structures subjected to a moving mass in the presence of 5% noise. It has also been shown that averaging the results obtained from noisy data collected through several repetitions of the experiment can improve the final prediction of the location and severity of damage.

Item Type: Article
Authors/Creators:Mousavi, M and Holloway, D and Olivier, JC and Gandomi, AH
Keywords: change detection, structural damage detection, vehicle bridge interaction, damage detection, static condensation, incomplete measurement
Journal or Publication Title: Measurement
Publisher: Elsevier Sci Ltd
ISSN: 0263-2241
DOI / ID Number: 10.1016/j.measurement.2020.108957
Copyright Information:

Copyright 2021 Elsevier Ltd.

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