Document Type

Journal Article

Publication Title

Engineering Structures

Volume

230

Publisher

Elsevier

School

School of Engineering

Funders

Research Grants Council of Hong Kong

Comments

Jin, N., Yang, Y. B., Dimitrakopoulos, E. G., Paraskeva, T. S., & Katafygiotis, L. S. (2021). Application of short-time stochastic subspace identification to estimate bridge frequencies from a traversing vehicle. Engineering Structures, 230, article 111688. https://doi.org/10.1016/j.engstruct.2020.111688

Abstract

© 2020 Elsevier Ltd This study establishes a short-time stochastic subspace identification (ST-SSI) framework to estimate bridge frequencies by processing the dynamic response of a traversing vehicle. The formulation uses a dimensionless description of the response that simplifies the vehicle-bridge interaction (VBI) problem and brings forward the minimum number of parameters required for the identification. With the aid of the dimensionless parameters the analysis manages to successfully apply ST-SSI despite the time-varying nature of the VBI system. Further, the proposed approach eliminates the adverse effect of the road surface roughness using a transformed residual vehicle response obtained from two traverses of a vehicle at different speeds over the bridge. The study verifies the proposed ST-SSI approach numerically: it first performs the dynamic VBI simulations to obtain the response of the vehicle, and then applies the proposed ST-SSI method, assuming the dynamic characteristics of the vehicle are available. The numerical experiments concern both a sprung mass model and a more realistic multi-degree-of-freedom (MDOF) vehicle model traversing a simply supported bridge. The results show that the proposed approach succeeds in identifying the first two bridge frequencies for test-vehicle speeds much higher (e.g., 10 m/s = 36 km/h and 20 m/s = 72 km/h) than previously considered, even in the presence of high levels of road surface roughness.

DOI

10.1016/j.engstruct.2020.111688

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Available for download on Wednesday, March 01, 2023

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