Completely non-contact modal testing of full-scale bridge in challenging conditions using vision sensing systems. (1st December 2022)

• Record Type: Journal Article
• Authors: Wang, Miaomin; Kei Ao, Wai; Bownjohn, James; Xu, Fuyou
• Is Part Of: Engineering structures. Volume 272(2022)
• Publication Date: 2022-12-01
• Subjects: Structural health monitoring -- Vision-based modal testing -- Full-scale bridge -- Completely noncontact measurement -- Torsional mode; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2022.114994
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: A new vision-based modal testing framework is proposed to address many issues of existing methods in field applications. The framework can capture not only bending but also torsional modal properties of full-scale bridges. The framework has high robustness in challenging conditions and no artificial target requirement. The efficacy of the framework was verified by conducting modal testing on a large-scale bridge prototype and an in-service suspension footbridge. Abstract: Vision-based modal testing method can provide a contactless approach to understanding dynamic properties of bridges. However, the applicability of this method to in-service bridge measurement has been limited by its drawbacks, such as low-accuracy measurement data of entire bridges, low robustness to varying lighting, artificial target installation or distinct natural features requirement, etc. In addition, the existing vision-based methods rarely captured torsional modes of the real-life bridges, leading to an insufficient understanding of the fundamental dynamic properties of the structures. This paper proposes a new vision-based modal testing framework to overcome these drawbacks. A multiple-setup strategy with three time-synchronised cameras is used to record videos of dynamic structural motion. A robust object tracking technique is used to extract reliable displacement of the field structure from the videos with significant noises. This framework provides a completely non-contact approach to capture bending and torsional modal properties of full-scale bridges in uncontrolled field conditions. A large-scale laboratory bridge and in-service suspension footbridge were selected to verify the feasibility of the proposed approach to extract correlated modal properties. The modal analysis results showed a satisfactory agreement with the accelerometer measurement, demonstrating the proposed framework's efficacy in operational modal analysis.