Applications of Machine Learning to Wind Engineering

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