Year: 2025
Type: conference-paper
Abstract: : Bridge vortex induced vibration (VIV) is a resonance phenomenon caused by the periodic shedding of vortices generated by natural wind passing through bridges. Bridge VIVs will not only cause fatigue damage to the structures but also affect driving safety for the passing vehicles. With the popularization of structural health monitoring (SHM) systems, machine learning technology is widely used in the field of vortex induced vibration identification for long-span bridges, due to its intelligence, real-time performance, and sensitivity to data. However, although traditional machine learning algorithms can identify the VIV based on the response data history of long-span bridges collected by SHM systems, they are difficult to apply to bridges which do not have historical vortex induced vibration data. Therefore, this paper proposes an adaptive transfer learning method for identifying VIV in the main girder of long-span suspension bridges. The proposed method can identify VIV without VIV history data of the target bridge. Results show that it can well identify VIVs at the earlier stage based on the SHM datasets of two long-span suspension bridges, verifying its effectiveness and generalization ability.
Source: Materials research proceedings
Author: Puyu Li
Institution: Southeast University
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Awards: G4352928677, G5967259483, G7474095455
OpenAlex ID: W4408405566