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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.
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. Wind engineering is an interdisciplinary field to provide rational treatment of interaction between the atmospheric boundary-layer winds and human activities Cermak There is a long and significant history for machine learning ML applications in several subfields involved in wind engineering, such as fluid mechanics Brunton et al.
The application of statistical learning to turbulence modeling in early s Kolmogorov and perceptron learning to structural design in late s Adeli and Yeh are representative examples.
