Applications of Machine Learning to Wind Engineering

Frontiers in Built Environment

Teng Wu¹  Reda Snaiki² 

[1] Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY, United States;Department of Construction Engineering, École de Technologie Supérieure, University of Quebec, Montreal, QC, Canada;

关键词: machine learning;    wind engineering;    wind climate;    terrain and topography;    aerodynamics and aeroelasticity;    structural dynamics;   

DOI  :  10.3389/fbuil.2022.811460

来源: DOAJ

【 摘 要 】

Advances of the analytical, numerical, experimental and field-measurement approaches in wind engineering offers unprecedented volume of data that, together with rapidly evolving learning algorithms and high-performance computational hardware, provide an opportunity for the community to embrace and harness full potential of machine learning (ML). This contribution examines the state of research and practice of ML for its applications to wind engineering. In addition to ML applications to wind climate, terrain/topography, aerodynamics/aeroelasticity and structural dynamics (following traditional Alan G. Davenport Wind Loading Chain), the review also extends to cover wind damage assessment and wind-related hazard mitigation and response (considering emerging performance-based and resilience-based wind design methodologies). This state-of-the-art review suggests to what extend ML has been utilized in each of these topic areas within wind engineering and provides a comprehensive summary to improve understanding how learning algorithms work and when these schemes succeed or fail. Moreover, critical challenges and prospects of ML applications in wind engineering are identified to facilitate future research efforts.

【 授权许可】

Unknown