Scaling wind loads for incremental dynamic analysis applications

Incremental Dynamic Analysis is a powerful tool for the performance assessment of structures where a full range of responses can be mapped. Currently, an open discussion among researchers is the scaling of wind loads at increasing hazard intensities. In common practice, local aerodynamic pressure data from wind tunnel testing are normalized with respect to the mean wind velocity. Then, the value is linearly scaled up to provide wind loads at considered limit states. The main issue in the linear scaling of winds is the non-consideration of cross-correlation between different time histories and the mean wind velocity. To address this issue, the Wieringa gust model is applied to account for the dependency of gustiness on mean wind speed, thereby updating the scaling coefficients for both mean and turbulent wind components. This methodology is demonstrated through the application of wind IDA on a high-rise steel hospital in Montreal, Canada. The building is designed to meet the code requirements for wind and earthquake loads. Finite element models that incorporate geometrical and material nonlinearities of building's lateral force-resisting systems are developed in OpenSees. These nonlinear models are used to analyze the impact of linear gust scaling on the building's performance under varying wind intensities.

Authors

Institutions

Publication Details

Journal
Journal of Wind Engineering and Industrial Aerodynamics
Published
2025-05-02
DOI
https://doi.org/10.1016/j.jweia.2025.106116
Citations
1
Primary Topic
Seismic Performance and Analysis
Type
article
Field-Weighted Citation Impact
0.95
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Scaling wind loads for incremental dynamic analysis applications

Lucia Tirca, Anastasia Athanasiou, Ted Stathopoulos
1 citations
Journal of Wind Engineering and Industrial Aerodynamics
Seismic Performance and Analysis
0.95
article

Scaling wind loads for incremental dynamic analysis applications

Lucia Tirca, Anastasia Athanasiou, Ted Stathopoulos
article en
1 citations

Abstract

Incremental Dynamic Analysis is a powerful tool for the performance assessment of structures where a full range of responses can be mapped. Currently, an open discussion among researchers is the scaling of wind loads at increasing hazard intensities. In common practice, local aerodynamic pressure data from wind tunnel testing are normalized with respect to the mean wind velocity. Then, the value is linearly scaled up to provide wind loads at considered limit states. The main issue in the linear scaling of winds is the non-consideration of cross-correlation between different time histories and the mean wind velocity. To address this issue, the Wieringa gust model is applied to account for the dependency of gustiness on mean wind speed, thereby updating the scaling coefficients for both mean and turbulent wind components. This methodology is demonstrated through the application of wind IDA on a high-rise steel hospital in Montreal, Canada. The building is designed to meet the code requirements for wind and earthquake loads. Finite element models that incorporate geometrical and material nonlinearities of building's lateral force-resisting systems are developed in OpenSees. These nonlinear models are used to analyze the impact of linear gust scaling on the building's performance under varying wind intensities.

Journal of Wind Engineering and Industrial AerodynamicsVol. 263
Bauhaus-Universität Weimar (DE), Concordia University (CA)
Affordable and clean energy
Openalex Percentile: Top 26%
Seismic Performance and Analysis
0.95
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.