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.
Highlights
Wind engineering is an interdisciplinary field to provide rational treatment of interaction between the atmospheric boundary-layer winds and human activities (Cermak 1975)
- Good simulation results - Significant optimization of the mean drag coefficient and standard deviation of the lift coefficient - Both specific directdomain and crossdomain knowledge are leveraged through transfer-learning and meta-learning - The deep deterministic policy gradient algorithm (DDPG) was used for the reinforcement learning (RL) algorithm - RL-based shape optimizer outperformed the basic gradient descent, particle swarm optimization (PSO) and typical RL without knowledge -The Twin Delayed Deep Deterministic policy gradient algorithm was selected as the RLalgorithm to update the agent - The RL-agent was capable to efficiently learn a control strategy, for both experiment and simulation, that will allow the reattachment of flow behind the cylinder and reduce the drag coefficient Acceptable performance (Continued on following page)
A total of 65 machine learning (ML) algorithms were reviewed in terms of their applications to each topical area of wind engineering, namely wind climate, terrain/topography, aerodynamics/aeroelasticity, structural dynamics, wind damage assessment and wind-related hazard mitigation and response
Summary
Wind engineering is an interdisciplinary field to provide rational treatment of interaction between the atmospheric boundary-layer winds and human activities (Cermak 1975). ML Applications to Wind Engineering high-performance computational hardware, 2) unprecedented volume of data generated with improved wind engineering techniques and methodologies, and 3) urgent needs for more accurate and efficient learning and modeling of complex phenomena in wind-related problems. The great success of AlexNet (a deep CNN on GPU) is essentially attributed to its ability to leverage GPU for training (Krizhevsky et al, 2012) Equipped with both sophisticated algorithms and advanced computational hardware, the learning machine (LM) is driven by data. To address the emerging challenges, datadriven machine learning offers a promising approach that is capable of processing big data in wind engineering field as well as modeling associated complex phenomena with high computational efficiency and simulation accuracy. While ML can augment the analytical approaches [e.g., data-driven discovery of closure models (Raissi et al, 2019)], numerical schemes [e.g., data-driven turbulence modeling (Duraisamy et al, 2019)], experimental tests [e.g., data-driven active control of transient wind simulation (Li et al, 2021a)] and field measurements [e.g., data-driven sparse sensor placement (Manohar et al, 2018)] in wind engineering, the review only focuses on its role to complement existing methodologies and potentially extend/transform current lines of wind engineering research and practice
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