Document Type

Journal Article

Publication Title

Engineering Structures






School of Engineering




Research Grants Council of Hong Kong


This is an author's accepted manuscript of: 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.


© 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.



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