IABSE Task Group 3.1 Benchmark Results. Part 2: Numerical Analysis of a Three-Degree-of-Freedom Bridge Deck Section Based on Experimental Aerodynamics

Diana, Giorgio; Stoyanoff, Stoyan; Aas-Jakobsen, Ketil; Allsop, Andrew; Andersen, Michael; Argentini, Tommaso; Montoya, Miguel Cid; Hernández, Santiago; Jurado, J. Á.; Katsuchi, Hiroshi; Kavrakov, Igor; Kim, Ho-Kyung; Larose, Guy L.; Larsen, Allan; Morgenthal, Guido; Øiseth, Ole; Omarini, Simone; Rocchi, Daniele; Svendsen, Martin N.; Wu, Teng

doi:10.1080/10168664.2019.1661331

Cited by 18 publications

(2 citation statements)

References 23 publications

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“…The classical buffeting response models for bridges used, for example, in Ref. [21] relies on stationary wind fields. However, this assumption is not always true, see for example Refs.…”

Section: Stationaritymentioning

confidence: 99%

Full-Scale Monitoring of the Wind Field, Surface Pressures and Structural Response of Gjemnessund Suspension Bridge

Consultant

Isaksen

President

2021

Structural Engineering International

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A custom-made monitoring system for long-term measurements of the wind field, surface pressures and structural response has been installed on the Gjemnessund Bridge in Norway. The monitoring system has been operating from 2018 to 2021. The scope was to investigate several topics within bridge aerodynamics in a fullscale environment, e.g. vortex shedding, gust response and span-wise correlations. Six cross-sectional strips with pressure taps were installed on the bridge. The pressure strips covered a span-wise distance equal to approximately six times the deck width. The upstream and the wake flow properties were measured in five locations along the same span. A case study showed that the ten-second gust correlations were stronger than the ten-minute correlations, both spanwise and section-wise. The gust impact on the bridge caused peak-to-peak midspan amplitudes of 1 m vertically and 2 m laterally.

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“…The classical buffeting response models for bridges used, for example, in Ref. [21] relies on stationary wind fields. However, this assumption is not always true, see for example Refs.…”

Section: Stationaritymentioning

confidence: 99%

Full-Scale Monitoring of the Wind Field, Surface Pressures and Structural Response of Gjemnessund Suspension Bridge

Consultant

Isaksen

President

2021

Structural Engineering International

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“…Petrini et al (2007), Wu and Kareem (2013b), Kavrakov and Morgenthal (2017), Kavrakov and Morgenthal (2018b), Øiseth et al (2011), Diana et al (2013)). Moreover, the task group "Super-long span bridge aerodynamics" of the International Association of Bridge and Structural Engineering (IABSE) is currently establishing a benchmark example for computer code verification and model validation for buffeting and flutter analyses of bridges (Diana et al 2019a;Diana et al 2019b). Within this group, results from different computer codes of various research groups and companies are compared within the activities of this IABSE working group.…”

Section: Introductionmentioning

confidence: 99%

Comparison Metrics for Time-Histories: Application to Bridge Aerodynamics

2020

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Wind effects can be critical for the design of lifelines such as long-span bridges. The existence of a significant number of aerodynamic force models, used to assess the performance of bridges, poses an important question regarding their comparison and validation. This study utilizes a unified set of metrics for a quantitative comparison of time-histories in bridge aerodynamics with a host of characteristics. Accordingly, nine comparison metrics are included to quantify the discrepancies in local and global signal features such as phase, time-varying frequency and magnitude content, probability density, nonstationarity and nonlinearity. Among these, seven metrics available in the literature are introduced after recasting them for time-histories associated with bridge aerodynamics. Two additional metrics are established to overcome the shortcomings of the existing metrics. The performance of the comparison metrics is first assessed using generic signals with prescribed signal features. Subsequently, the metrics are applied to a practical example from bridge aerodynamics to quantify the discrepancies in the aerodynamic forces and response based on numerical and semi-analytical aerodynamic models. In this context, it is demonstrated how a discussion based on the set of comparison metrics presented here can aid a model evaluation by offering deeper insight. The outcome of the study is intended to provide a framework for quantitative comparison and validation of aerodynamic models based on the underlying physics of fluid-structure interaction. Immediate further applications are expected for the comparison of time-histories that are simulated by data-driven approaches.

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