1. 哈尔滨工业大学 土木工程学院,哈尔滨,150090
2. 哈尔滨工业大学 结构工程灾变与控制教育部重点实验室,哈尔滨,150090
3. 哈尔滨工业大学 土木工程智能防灾减灾工业与信息化部重点实验室,哈尔滨,150090
张泽宇(1997—),博士研究生,研究方向:桥梁风工程与机器学习. Email:605027201@qq.com
赖马树金*(1983—),教授,研究方向:桥梁风工程与机器学习,流固耦合. E-mail:laimashujin@hit.edu.cn
纸质出版:2025
移动端阅览
张泽宇, 周旭曦, 许楠, 等. 大型结构风效应流固耦合机器学习研究进展[J]. 空气动力学学报, 2025, 43(5): 53−77.
ZHANG Zeyu, ZHOU Xuxi, XU Nan, et al. Advances in machine learning for wind-induced fluid-structure interaction of large-scale structures[J]. 2025, 43(5): 53-77.
张泽宇, 周旭曦, 许楠, 等. 大型结构风效应流固耦合机器学习研究进展[J]. 空气动力学学报, 2025, 43(5): 53−77. DOI: 10.7638/kqdlxxb-2025.0061
ZHANG Zeyu, ZHOU Xuxi, XU Nan, et al. Advances in machine learning for wind-induced fluid-structure interaction of large-scale structures[J]. 2025, 43(5): 53-77. DOI: 10.7638/kqdlxxb-2025.0061.
随着计算技术与数据科学的迅速发展,机器学习为解决大型结构风效应中复杂流固耦合问题提供了全新的研究范式。本文系统综述了机器学习在大型结构风效应领域的研究进展,涵盖了结构表面风压预测、结构风致响应分析与建模、气动力方程智能识别以及基于强化学习的结构振动控制4个主要研究方向。具体而言,结构表面风压预测方面,机器学习能够精准地挖掘结构表面复杂非线性风压场特征;结构风致响应分析与建模中,机器学习有效实现了大型结构异常大幅振动识别与精细化建模;对于气动力方程智能识别领域,基于数据驱动的机器学习方法大幅提高了非线性方程识别的自动化程度与准确性;在结构振动控制方面,强化学习实现了实时、高效的主动控制策略优化。然而,当前研究在数据融合、模型泛化性与物理可解释性方面仍存在明显不足。未来的研究需进一步融合物理机制与数据驱动模型,构建具备高泛化性、鲁棒性和物理解释能力的机器学习模型,推动结构风工程进一步智能化发展。
With the rapid development of computational technology and data science
machine learning provides a novel research paradigm for addressing complex fluid-structure interaction problems in large-scale structural wind effects. This paper systematically reviews recent advances in machine learning applications for wind effects on large-scale structures
focusing on four key aspects: structural surface wind pressure prediction
wind-induced response analysis and modeling
intelligent identification of aerodynamic equations
and reinforcement learning-based structural vibration control. For structural surface wind pressure prediction
machine learning effectively captures complex nonlinear wind pressure characteristics on structural surfaces. In the analysis and modeling of structural wind-induced responses
machine learning techniques enables accurate identification and modeling of abnormal large-amplitude vibrations of large-scale structures. Regarding intelligent identification of aerodynamic equations
data-driven machine learning significantly enhances the automation and accuracy of nonlinear equation identification. For structural vibration control
reinforcement learning offers optimized real-time active control strategies. However
challenges persist in data fusion
model generalization
and physical interpretability. Future studies should integrate physical mechanisms with data-driven models to develop machine learning approaches characterized by high generalization
robustness
and physical interpretability
thus further advancing the intelligent development of structural wind engineering.
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