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The application of reduced order models (ROMs) in the aerodynamic/aeroelastic analysis of long-span bridges, unlike the aeronautical structures, has not been extensively studied. ROMs are computationally efficient techniques, which have been widely used for predicting unsteady aerodynamic response of airfoils and wings. This paper aims to discuss the application of a reduced order computational fluid dynamics (CFD) model based on the eigensystem realization algorithm (ERA) in the aeroelastic analysis of the Great Belt Bridge (GBB).

The aerodynamic impulse response of the GBB section is used to construct the aerodynamic ROM, and then the aerodynamic ROM is coupled with the reduced DOF model of the system to construct the aeroelastic ROM. Aerodynamic coefficients and flutter derivatives are evaluated and compared to those of the advanced discrete vortex method-based CFD code.

Results demonstrate reasonable prediction power and high computational efficiency of the technique that can serve for preliminary aeroelastic analysis and design of long-span bridges, optimization and control purposes.

The application of a system identification tool like ERA into the aeroelastic analysis of long-span bridges is performed for the first time in this work. Authors have developed their earlier work on the aerodynamic analysis of long-span bridges, published in the Journal of Bridge Engineering, by coupling the aerodynamic forces with reduced DOF of structural system. The high computational efficiency of the technique enables bridge designers to perform preliminary aeroelastic analysis of long-span bridges in less than a minute.

The purpose of this paper is to further validate a wireless sensor system developed at Clarkson University for structural monitoring of highway bridges. The particular bridge monitored employs a fiber reinforced polymer (FRP) panel system which is fairly innovative in the field of civil engineering design. The superstructure was monitored on two separate occasions to determine a change in structural response and see how the structural system performs over time.

A series of wireless sensor units was deployed at various locations of the steel superstructure, to measure both the modal response from acceleration measurements as well as quasi‐static and dynamic strain response. Ambient and forced loading conditions were applied to measure the response. Data results were compared over two separate periods approximately nine months apart.

The first eight mode shapes were produced from output‐only system identification providing natural frequencies ranging from approximately 6 to 42 Hz. The strain response was monitored over two different testing periods to measure various performance characteristics. Neutral axis, distribution factor, impact factor and end fixity were determined. Results appeared to be different over the two testing periods. They indicate that the load rating of the superstructure decreased over the nine month period, possibly due to deterioration of the materials or composite action.

The results from the two testing periods indicate that further testing needs to be completed to validate the change in response. It is difficult to say with certainty that the significant change in response is due to bridge deterioration and not other factors such as temperature effects on load rating. The sensor system, however, proved to provide high quality data and responses indicating its successful deployment for load testing and monitoring of highway infrastructure.

The paper provides a depiction of the change in structural behavior of a bridge superstructure using a wireless sensor system. The wireless system provided high‐rate data transmission in real time. Load testing at two different points in time, eight months apart, showed a significant change in bridge behavior. The paper provides a practical and actual physical load test and rating during these two periods for quantifiable change in response. It is shown that the wireless system is capable of infrastructure monitoring and that possible deterioration is expected with this particular bridge design. Additionally, the load testing occurred during different seasons, which could create cause for temperature effects in load rating. This can provide a basis for future performance monitoring techniques and structural health monitoring.