Applications of Machine Learning to Wind Engineering

TLDR

Advances in analytical, numerical, experimental, and field‑measurement approaches in wind engineering have generated vast data, and combined with evolving learning algorithms and high‑performance computing, this enables the community to fully harness machine learning. This review examines the current state of research and practice of machine learning in wind engineering and identifies critical challenges and prospects to guide future work. The review covers ML applications to wind climate, terrain/topography, aerodynamics/aeroelasticity, structural dynamics, wind damage assessment, and wind‑related hazard mitigation and response, including emerging performance‑based and resilience‑based design methodologies. The state‑of‑the‑art review summarizes how machine learning has been applied across these areas, clarifies how learning algorithms function, and delineates when they succeed or fail.

Abstract

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.