Advances in machine learning for wind-induced fluid-structure interaction of large-scale structures

ZHANG Zeyu; ZHOU Xuxi; XU Nan; WHANG Haowei; YANG Zijian; ZHUANG Jian; LI Shanwu; LAIMA Shujin; CHEN Wenli; LI Hui

doi:10.7638/kqdlxxb-2025.0061

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

Advances in machine learning for wind-induced fluid-structure interaction of large-scale structures

更新时间：2026-05-28

- Advances in machine learning for wind-induced fluid-structure interaction of large-scale structures
- Vol. 43, Issue 5, Pages: 53-77(2025)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.7638/kqdlxxb-2025.0061
  CLC： V211.3;TP183;TU311.3
- Published：2025
- 稿件说明：
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.

摘要

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。

Abstract

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.

关键词

Keywords

references

ZHAO L, CUI W, SHEN X M, et al. A fast on-site measure-analyze-suppress response to control vortex-induced-vibration of a long-span bridge[J]. Structures, 2022, 35: 192−201. doi: 10.1016/j.istruc.2021.10.017

SHARMA A, MITTAL H, GAIROLA A. Mitigation of wind load on tall buildings through aerodynamic modifications: Review[J]. Journal of Building Engineering, 2018, 18: 180−194. doi: 10.1016/j.jobe.2018.03.005

LIVANOS D. Investigation of vortex induced vibrations on wind turbine towers[D]. Delft, Netherlands: Technische Universiteit Delft, 2018.

DAVENPORT A G. The application of statistical concepts to the wind loading of structures[J]. Proceedings of the Institution of Civil Engineers, 1961, 19(4): 449−472. doi: 10.1680/iicep.1961.11304

BORRI C, ZHOU S, CHEN Z Q. Coupling investigation on vortex-induced vibration and galloping of rectangular cylinders[C]// 7th International Colloquium on Bluff Body Aerodynamics and Applications, Shanghai, 2012.

AMMANN OTHMAR H, VON KÁRMÁN T, WOODRUFF G B. The failure of the Tacoma Narrows bridge[R]. Federal Works Agency, Washington D. C. , 1941.

ZHU L D, XU Y L. Buffeting response of long-span cable-supported bridges under skew winds. Part 1: Theory[J]. Journal of Sound and Vibration, 2005, 281(3−5): 647−673. doi: 10.1016/j.jsv.2004.01.026

SCANLAN R H. The action of flexible bridges under wind, Ⅱ: Buffeting theory[J]. Journal of Sound and Vibration, 1978, 60(2): 201−211. doi: 10.1016/S0022-460X(78)80029-7

WANG H C, FU T F, DU Y Q, et al. Scientific discovery in the age of artificial intelligence[J]. Nature, 2023, 620(7972): 47−60. doi: 10.1038/s41586-023-06221-2

JI J, LI H. State-of-the-arts in artificial intelligence for disaster prevention and mitigation of civil engineering[J]. Bulletin of National Natural Science Foundation of China, 2023, 37(5): 840−853(in Chinese).

LAI M, LI W J, FENG H, et al. Research progress of machine learning in bridge wind engineering[J]. China Journal of Highway and Transport, 2023, 36(8): 1−13(in Chinese).

HORNIK K, STINCHCOMBE M, WHITE H. Multilayer feedforward networks are universal approximators[J]. Neural Networks, 1989, 2(5): 359−366. doi: 10.1016/0893-6080(89)90020-8

RUMELHART D E, HINTON G E, WILLIAMS R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323: 533−536. doi: 10.1038/323533a0

KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84−90. doi: 10.1145/3065386

MANDIC D P, CHAMBERS J. Recurrent neural networks for prediction: Learning algorithms, architectures and stability[M]. John Wiley Sons, Inc. , New York, 2001.

MURTAGH F, CONTRERAS P. Algorithms for hierarchical clustering: An overview[J]. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2012, 2(1): 86−97. doi: 10.1002/widm.53

VINCENT P, LAROCHELLE H, LAJOIE I, et al. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion[J]. Journal of Machine Learning Research, 2010, 11: 3371−3408.

KINGMA D P, WELLING M. An introduction to variational autoencoders[J]. Foundations and Trends® in Machine Learning, 2019, 12(4): 307−392. doi: 10.1561/2200000056

BELLMAN R. A Markovian decision process[J]. Indiana University Mathematics Journal, 1957, 6(4): 679−684. doi: 10.1512/iumj.1957.6.56038

RAISSI M, PERDIKARIS P, KARNIADAKIS G E. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations[J]. Journal of Computational Physics, 2019, 378: 686−707. doi: 10.1016/j.jcp.2018.10.045

LU L, MENG X H, MAO Z P, et al. DeepXDE: A deep learning library for solving differential equations[J]. SIAM Review, 2021, 63(1): 208−228. doi: 10.1137/19m1274067

CHEN Z, LIU Y, SUN H. Physics-informed learning of governing equations from scarce data[J]. Nature Communications, 2021, 12: 6136. doi: 10.1038/s41467-021-26434-1

ZHANG Z Y, LI S W, FENG H, et al. Machine learning for bridge wind engineering[J]. Advances in Wind Engineering, 2024, 1(1): 100002. doi: 10.1016/j.awe.2024.100002

LAIMA S J, ZHOU X X, JIN X W, et al. DeepTRNet: Time-resolved reconstruction of flow around a circular cylinder via spatiotemporal deep neural networks[J]. Physics of Fluids, 2023, 35(1): 015118. doi: 10.1063/5.0129049

ZHOU X X, JIN X W, LAIMA S J, et al. Large-scale flow field super-resolution via local-global fusion convolutional neural networks[J]. 2024, 36(5): 055130. DOI:10.1063/5.0207575

JIANG C, VINUESA R, CHEN R L, et al. An interpretable framework of data-driven turbulence modeling using deep neural networks[J]. Physics of Fluids, 2021, 33(5): 055133. doi: 10.1063/5.0048909

BRUNTON S L, NOACK B R, KOUMOUTSAKOS P. Machine learning for fluid mechanics[J]. Annual Review of Fluid Mechanics, 2020, 52: 477−508. doi: 10.1146/annurev-fluid-010719-060214

ZHAO S, ZHANG C T, DAI X X, et al. Review of wind-induced effects estimation through nonlinear analysis of tall buildings, high-rise structures, flexible bridges and transmission lines[J]. Buildings, 2023, 13(8): 2033. doi: 10.3390/buildings13082033

MOSTAFA K, ZISIS I, MOUSTAFA M A. Machine learning techniques in structural wind engineering: A state-of-the-art review[J]. Applied Sciences, 2022, 12(10): 5232. doi: 10.3390/app12105232

WU T, SNAIKI R. Applications of machine learning to wind engineering[J]. Frontiers in Built Environment, 2022, 8: 811460. doi: 10.3389/fbuil.2022.811460

KAREEM A. Emerging frontiers in wind engineering: Computing, stochastics, machine learning and beyond[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2020, 206: 104320. doi: 10.1016/j.jweia.2020.104320

CHEN Y, KOPP G A, SURRY D. Interpolation of wind-induced pressure time series with an artificial neural network[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2002, 90(6): 589−615. doi: 10.1016/S0167-6105(02)00155-1

FU J Y, LI Q S, XIE Z N. Prediction of wind loads on a large flat roof using fuzzy neural networks[J]. Engineering Structures, 2006, 28(1): 153−161. doi: 10.1016/j.engstruct.2005.08.006

FU J Y, LIANG S G, LI Q S. Prediction of wind-induced pressures on a large gymnasium roof using artificial neural networks[J]. Computers Structures, 2007, 85(3−4): 179−192. doi: 10.1016/j.compstruc.2006.08.070

GAVALDA X, FERRER-GENER J, KOPP G A, et al. Interpolation of pressure coefficients for low-rise buildings of different plan dimensions and roof slopes using artificial neural networks[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2011, 99(5): 658−664. doi: 10.1016/j.jweia.2011.02.008

BRE F, GIMENEZ J M, FACHINOTTI V D. Prediction of wind pressure coefficients on building surfaces using artificial neural networks[J]. Energy and Buildings, 2018, 158: 1429−1441. doi: 10.1016/j.enbuild.2017.11.045

NI Y Q, LI M. Wind pressure data reconstruction using neural network techniques: A comparison between BPNN and GRNN[J]. Measurement, 2016, 88: 468−476. doi: 10.1016/j.measurement.2016.04.049

HUANG Y Q, OU G H, FU J Y, et al. Prediction of mean and RMS wind pressure coefficients for low-rise buildings using deep neural networks[J]. Engineering Structures, 2023, 274: 115149. doi: 10.1016/j.engstruct.2022.115149

TIAN J Q, GURLEY K R, DIAZ M T, et al. Low-rise gable roof buildings pressure prediction using deep neural networks[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2020, 196: 104026. doi: 10.1016/j.jweia.2019.104026

DING Z X, ZHANG W, ZHU D P. Neural-network based wind pressure prediction for low-rise buildings with genetic algorithm and Bayesian optimization[J]. Engineering Structures, 2022, 260: 114203. doi: 10.1016/j.engstruct.2022.114203

HU G, LIU L B, TAO D C, et al. Deep learning-based investigation of wind pressures on tall building under interference effects[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2020, 201: 104138. doi: 10.1016/j.jweia.2020.104138

HU G, KWOK K C S. Predicting wind pressures around circular cylinders using machine learning techniques[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2020, 198: 104099. doi: 10.1016/j.jweia.2020.104099

MEDDAGE D P P, EKANAYAKE I U, WEERASURIYA A U, et al. Explainable Machine Learning (XML) to predict external wind pressure of a low-rise building in urban-like settings[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2022, 226: 105027. doi: 10.1016/j.jweia.2022.105027

DU K, CHEN B. A hybrid semi-supervised regression based machine learning method for predicting peak wind loads on a group of buildings[J]. Engineering Structures, 2023, 275: 115245. doi: 10.1016/j.engstruct.2022.115245

JIN X W, CHENG P, CHEN W L, et al. Prediction model of velocity field around circular cylinder over various Reynolds numbers by fusion convolutional neural networks based on pressure on the cylinder[J]. Physics of Fluids, 2018, 30(4): 047105. doi: 10.1063/1.5024595

MI J Y, JIN X W, LI H. Cascade-Net for predicting cylinder wake at Reynolds numbers ranging from subcritical to supercritical regime[J]. Physics of Fluids, 2023, 35(7): 075132. doi: 10.1063/5.0155649

YAN M T, ZHANG Z M, GAO S C, et al. Predicting aerodynamic pressure on a square cylinder from wake velocity field by masked gated recurrent unit model[J]. Physics of Fluids, 2022, 34(11): 115101. doi: 10.1063/5.0110491

RAISSI M, WANG Z C, TRIANTAFYLLOU M S, et al. Deep learning of vortex-induced vibrations[J]. Journal of Fluid Mechanics, 2019, 861: 119−137. doi: 10.1017/jfm.2018.872

LIU J L, SHUM K, TSE T K T, et al. Bidirectional prediction between wake velocity and surface pressure using deep learning techniques[J]. Physics of Fluids, 2024, 36(2): 025162. doi: 10.1063/5.0191568

HUANG D M, HE S Q, HE X H, et al. Prediction of wind loads on high-rise building using a BP neural network combined with POD[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2017, 170: 1−17. doi: 10.1016/j.jweia.2017.07.021

KOU J Q, ZHANG W W. Dynamic mode decomposition and its applications in fluid dynamics[J]. ACTA AERODYNAMICA SINICA, 2018, 36(2): 163−179. doi: 10.7638/kqdlxxb-2017.0134

ZHANG W W, DOU Z H, LI X T, et al. Various flow-induced vibrations of bridges and von Kármán vortex street[J]. ACTA AERODYNAMICA SINICA, 2020, 38(3): 405−412. doi: 10.7638/kqdlxxb-2020.0088

SCANLAN R H, TOMKO J J. Airfoil and bridge deck flutter derivatives[J]. Journal of the Engineering Mechanics Division, 1971, 97(6): 1717−1737. doi: 10.1061/jmcea3.0001526

CHEN C H. Determination of flutter derivatives via a neural network approach[J]. Journal of Sound and Vibration, 2003, 263(4): 797−813. doi: 10.1016/S0022-460X(02)01279-8

CHEN C H, WU J C, CHEN J H. Prediction of flutter derivatives by artificial neural networks[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2008, 96(10−11): 1925−1937. doi: 10.1016/j.jweia.2008.02.044

CHUNG J Y, LEE S W, CHANG S K, et al. Estimation of flutter derivatives of various sections using numerical simulation and neural network[C]// The 2012 World Congress on Advances in Civil, Environmental, and Materials Research (ACEM’ 12), Seoul, Korea, 2012.

WANG Z L, ZHAO L, WEI W. Active aerodynamic flap flutter control optimization for bridges driven by neural network[J]. China Journal of Highway and Transportation, 2023, 36(8): 32−41.

LI Y, LI C, LIANG Y D, et al. Fast prediction of the flutter critical wind speed of streamlined box girders by using aerostatic force coefficients and artificial neural networks[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2022, 222: 104939. doi: 10.1016/j.jweia.2022.104939

JUNG S, GHABOUSSI J, KWON S D. Estimation of aeroelastic parameters of bridge decks using neural networks[J]. Journal of Engineering Mechanics, 2004, 130(11): 1356−1364. doi: 10.1061/(asce)0733-9399(2004)130:11(1356

LUTE V, UPADHYAY A, SINGH K K. Support vector machine based aerodynamic analysis of cable stayed bridges[J]. Advances in Engineering Software, 2009, 40(9): 830−835. doi: 10.1016/j.advengsoft.2009.01.008

LIAO H L, MEI H Y, HU G, et al. Machine learning strategy for predicting flutter performance of streamlined box girders[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2021, 209: 104493. doi: 10.1016/j.jweia.2020.104493

ZHU G S, SHAO Y H. Reliability analysis of bridge flutter based on neural network and adaptive stochastic sampling[J]. Journal of Hefei University of Technology (Natural Science), 2022, 45(06): 770−777.

TINMITONDÉ S, HE X H, YAN L, et al. Data-driven prediction of critical flutter velocity of long-span suspension bridges using a probabilistic machine learning approach[J]. Computers Structures, 2023, 280: 107002. doi: 10.1016/j.compstruc.2023.107002

WU T, KAREEM A, GE Y J. Linear and nonlinear aeroelastic analysis frameworks for cable-supported bridges[J]. Nonlinear Dynamics, 2013, 74(3): 487−516. doi: 10.1007/s11071-013-0984-7

ABBAS T, KAVRAKOV I, MORGENTHAL G, et al. Prediction of aeroelastic response of bridge decks using artificial neural networks[J]. Computers Structures, 2020, 231: 106198. doi: 10.1016/j.compstruc.2020.106198

WU T, KAREEM A. Modeling hysteretic nonlinear behavior of bridge aerodynamics via cellular automata nested neural network[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2011, 99(4): 378−388. doi: 10.1016/j.jweia.2010.12.011

ZHANG W M, FENG D D, ZHAO L M, et al. A deep learning-based method for full-bridge flutter analysis considering aerodynamic and geometric nonlinearities[J]. Computers Structures, 2025, 310: 107693. doi: 10.1016/j.compstruc.2025.107693

ZHAO L M, ZHANG W M, WANG Z W, et al. Nonlinear unsteady aerodynamic forces prediction and aeroelastic analysis of wind-induced bridge response at multiple wind speeds: A deep learning-based reduced-order model[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2024, 254: 105905. doi: 10.1016/j.jweia.2024.105905

LI W J, LAIMA S J, JIN X W, et al. A novel long short-term memory neural-network-based self-excited force model of limit cycle oscillations of nonlinear flutter for various aerodynamic configurations[J]. Nonlinear Dynamics, 2020, 100(3): 2071−2087. doi: 10.1007/s11071-020-05631-5

AN X Y, LI S P, WU T. Modeling nonlinear aeroelastic forces for bridge decks with various leading edges using LSTM networks[J]. Applied Sciences, 2023, 13(10): 6005. doi: 10.3390/app13106005

KAVRAKOV I, MORGENTHAL G, MCROBIE A. Data-driven aeroelastic analyses of structures in turbulent wind conditions using enhanced Gaussian Processes with aerodynamic priors[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2024, 253: 105848. doi: 10.1016/j.jweia.2024.105848

LI S W, LAIMA S J, LI H. Cluster analysis of winds and wind-induced vibrations on a long-span bridge based on long-term field monitoring data[J]. Engineering Structures, 2017, 138: 245−259. doi: 10.1016/j.engstruct.2017.02.024

DUAN M L, DONG H T. Cluster-analysis-based research on vortex-induced vibration fromWind tunnel tests of a sectional bridge model[J]. Structural Engineers, 2023, 39(4): 138−145.

HE M, LIANG P, ZHANG Y, et al. Identification, track ing and warning of vortex induced vibration using k -means clustering method[J ] . Structure and Infrastructure Engineering, 2024, 20(3): 380−393. doi: 10.1080/15732479.2022.2096081

ARUL M, KAREEM A, KWON D K. Identification of vortex-induced vibration of tall building pinnacle using cluster analysis for fatigue evaluation: Application to burj khalifa[J]. Journal of Structural Engineering, 2020, 146(11): 04020234. doi: 10.1061/(asce)st.1943-541x.0002799

LIM J, KIM S, KIM H K. Using supervised learning techniques to automatically classify vortex-induced vibration in long-span bridges[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2022, 221: 104904. doi: 10.1016/j.jweia.2022.104904

KIM S, KIM T. Machine-learning-based prediction of vortex-induced vibration in long-span bridges using limited information[J]. Engineering Structures, 2022, 266: 114551. doi: 10.1016/j.engstruct.2022.114551

HUANG M F, LI X Z, CAI K, et al. Motion adaptive vision-based vibration measurement and modal identification for the roof masts of a tall building[J]. Engineering Structures, 2025, 323: 119278. doi: 10.1016/j.engstruct.2024.119278

SU X, MAO J X, WANG H, et al. Deep learning-based automated identification on vortex-induced vibration of long suspenders for the suspension bridge[J]. Mechanical Systems and Signal Processing, 2025, 224: 112070. doi: 10.1016/j.ymssp.2024.112070

KIM S, LEE S H, KIM S. Pointwise multiclass vibration classification for cable-supported bridges using a signal-segmentation deep network[J]. Engineering Structures, 2023, 279: 115599. doi: 10.1016/j.engstruct.2023.115599

LEE S, KIM S. Pointwise vortex-induced vibration detection: Learning from synthetic time-series data[J]. Engineering Structures, 2025, 326: 119525. doi: 10.1016/j.engstruct.2024.119525

HOU J L, CAO S G, HU H, et al. Vortex-induced vibration recognition for long-span bridges based on transfer component analysis[J]. Buildings, 2023, 13(8): 2012. doi: 10.3390/buildings13082012

WAN C F, HOU J L, ZHANG G C, et al. Domain adaptation based automatic identification method of vortex induced vibration of long-span bridges without prior information[J]. Engineering Applications of Artificial Intelligence, 2025, 139: 109677. doi: 10.1016/j.engappai.2024.109677

LIN P F, HU G, LI C, et al. Machine learning-based prediction of crosswind vibrations of rectangular cylinders[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2021, 211: 104549. doi: 10.1016/j.jweia.2021.104549

WANG Z, ZHU J S, ZHANG Z T. Machine learning-based deep data mining and prediction of vortex-induced vibration of circular cylinders[J]. Ocean Engineering, 2023, 285: 115313. doi: 10.1016/j.oceaneng.2023.115313

LIU P, CHU X L, CUI W, et al. Bayesian inference based parametric identification of vortex-excited force using on-site measured vibration data on a long-span bridge[J]. Engineering Structures, 2022, 266: 114597. doi: 10.1016/j.engstruct.2022.114597

YAN W J, FENG Z Q, YANG W, et al. Bayesian inference for the dynamic properties of long-span bridges under vortex-induced vibration with Scanlan's model and dense optical flow scheme[J]. Mechanical Systems and Signal Processing, 2022, 174: 109078. doi: 10.1016/j.ymssp.2022.109078

MA Z X, LI J W, WANG F, et al. Prediction of vortex-induced vibration response of open bridge girder based on machine learning method[J]. International Journal of Structural Stability and Dynamics, 2024, 24(2): 2450017. doi: 10.1142/s0219455424500172

YAN Z X, ZHENG S X, TAI X Y, et al. Machine learning models for predicting VIV amplitude of streamlined steel box girders[J]. Structures, 2024, 63: 106444. doi: 10.1016/j.istruc.2024.106444

LI S W, KAISER E, LAIMA S J, et al. Discovering time-varying aerodynamics of a prototype bridge by sparse identification of nonlinear dynamical systems[J]. Physical Review E, 2019, 100(2−1): 022220. doi: 10.1103/PhysRevE.100.022220

YAN Z X, ZHENG S X, YANG F F, et al. A simplified approach to recognize vortex-induced vibration response using machine learning[J]. Structural Engineering International, 2024, 34(4): 670−682. doi: 10.1080/10168664.2023.2287460

IJAZ A, MANZOOR S. Vortex induced vibration prediction through machine learning techniques[J]. AIP Advances, 2024, 14(11): 115025. doi: 10.1063/5.0236511

CHEN Z S, ZHANG L K, LI K, et al. Machine-learning prediction of aerodynamic damping for buildings and structures undergoing flow-induced vibrations[J]. Journal of Building Engineering, 2023, 63: 105374. doi: 10.1016/j.jobe.2022.105374

SANYAL P, PAUL R, DALUI S K. Machine learning-based wind-induced response analysis in rectangular building models with limbs[J]. The Structural Design of Tall and Special Buildings, 2024, 33(12): e2116. doi: 10.1002/tal.2116

LI S W, LAIMA S J, LI H. Physics-guided deep learning framework for predictive modeling of bridge vortex-induced vibrations from field monitoring[J]. Physics of Fluids, 2021, 33(3): 037113. doi: 10.1063/5.0032402

WANG Y F, FENG H, XU N, et al. A data-driven model for predictive modeling of vortex-induced vibrations of a long-span bridge[J]. Applied Sciences, 2024, 14(6): 2233. doi: 10.3390/app14062233

LI S W, LAIMA S J, LI H. Data-driven modeling of vortex-induced vibration of a long-span suspension bridge using decision tree learning and support vector regression[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2018, 172: 196−211. doi: 10.1016/j.jweia.2017.10.022

CHENG C, MENG H, LI Y Z, et al. Deep learning based on PINN for solving 2 DOF vortex induced vibration of cylinder[J]. Ocean Engineering, 2021, 240: 109932. doi: 10.1016/j.oceaneng.2021.109932

TANG H S, LIAO Y Y, YANG H, et al. A transfer learning-physics informed neural network (TL-PINN) for vortex-induced vibration[J]. Ocean Engineering, 2022, 266: 113101. doi: 10.1016/j.oceaneng.2022.113101

BAI X D, ZHANG W. Machine learning for vortex induced vibration in turbulent flow[J]. Computers Fluids, 2022, 235: 105266. doi: 10.1016/j.compfluid.2021.105266

ZHU P, LIU Z L, XU Z Q, et al. An adaptive weight physics-informed neural network for vortex-induced vibration problems[J]. Buildings, 2025, 15(9): 1533. doi: 10.3390/buildings15091533

FENERCI A, ØISETH O. Measured buffeting response of a long-span suspension bridge compared with numerical predictions based on design wind spectra[J]. Journal of Structural Engineering, 2017, 143(9): 04017131. doi: 10.1061/(asce)st.1943-541x.0001873

DING Y, YE X W, GUO Y. A multistep direct and indirect strategy for predicting wind direction based on the EMD-LSTM model[J]. Structural Control and Health Monitoring, 2023, 2023(1): 4950487. doi: 10.1155/2023/4950487

YE X W, SUN Z, LU J. Prediction and early warning of wind-induced girder and tower vibration in cable-stayed bridges with machine learning-based approach[J]. Engineering Structures, 2023, 275: 115261. doi: 10.1016/j.engstruct.2022.115261

LIU C Y, GONG Y, JIANG Z S, et al. Buffeting analysis of a suspension bridge under construction based on adjacent wind field data[J]. Engineering Structures, 2022, 251: 113490. doi: 10.1016/j.engstruct.2021.113490

CASTELLON D F, FENERCI A, OLE Ø. A comparative study of wind-induced dynamic response models of long-span bridges using artificial neural networks, support vector regression and buffeting theory[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2021, 209: 104484. doi: 10.1016/j.jweia.2020.104484

HAREENDRAN S P, ALIPOUR A. Prediction of nonlinear structural response under wind loads using deep learning techniques[J]. Applied Soft Computing, 2022, 129: 109424. doi: 10.1016/j.asoc.2022.109424

HU P, CHENG W, XU G J, et al. Prediction of buffeting responses of the thin plate under joint action of wave and wind using LSTM and transfer learning[J]. Applied Ocean Research, 2023, 134: 103514. doi: 10.1016/j.apor.2023.103514

FANG C, TANG H J, LI Y L. Stochastic response assessment of cross-sea bridges under correlated wind and waves via machine learning[J]. Journal of Bridge Engineering, 2020, 25(6): 04020025. doi: 10.1061/(asce)be.1943-5592.0001554

FANG C, TANG H J, LI Y L, et al. Stochastic response of a cable-stayed bridge under non-stationary winds and waves using different surrogate models[J]. Ocean Engineering, 2020, 199: 106967. doi: 10.1016/j.oceaneng.2020.106967

ZHU S Y, YI R, LI Y L, et al. Buffeting reliability of high-rise bridge tower in mountain area based on CNN-BiLSTM[J]. Applied Soft Computing, 2024, 163: 111872. doi: 10.1016/j.asoc.2024.111872

CASTELLON D F, FENERCI A, PETERSEN Ø W, et al. Full long-term buffeting analysis of suspension bridges using Gaussian process surrogate modelling and importance sampling Monte Carlo simulations[J]. Reliability Engineering System Safety, 2023, 235: 109211. doi: 10.1016/j.ress.2023.109211

NIKOSE T J, SONPAROTE R S. Dynamic wind response of tall buildings using artificial neural network[J]. The Structural Design of Tall and Special Buildings, 2019, 28(13): e1657. doi: 10.1002/tal.1657

YUCEL M, BEKDAŞ G, NIGDELI S M. Machine learning-based model for prediction of optimum TMD parameters in time-domain history[J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2024, 46(4): 192. doi: 10.1007/s40430-024-04747-8

ZHANG Y W, SCHAUER T, BLEICHER A. Optimized passive/semi-active vibration control using distributed-multiple tuned facade damping system in tall buildings[J]. Journal of Building Engineering, 2022, 52: 104416. doi: 10.1016/j.jobe.2022.104416

LYSTAD T M, FENERCI A, OLE Ø. Buffeting response of long-span bridges considering uncertain turbulence parameters using the environmental contour method[J]. Engineering Structures, 2020, 213: 110575. doi: 10.1016/j.engstruct.2020.110575

LIU P, ZHAO L, FANG G S, et al. Explicit polynomial regression models of wind characteristics and structural effects on a long-span bridge utilizing onsite monitoring data[J]. Structural Control and Health Monitoring, 2021, 28(5): e2705. doi: 10.1002/stc.2705

ZHANG Y M, WANG H, MAO J X, et al. Probabilistic framework with Bayesian optimization for predicting typhoon-induced dynamic responses of a long-span bridge[J]. Journal of Structural Engineering, 2021, 147(1): 04020297. doi: 10.1061/(asce)st.1943-541x.0002881

TAO T Y, DENG P, WANG H, et al. Intelligent prediction of buffeting responses of long-span bridge under the action of thunderstorm winds[J]. China Journal of Highway and Transportation, 2023, 36(8): 87−95.

KAVRAKOV I. Buffeting analysis of structures using Gaussian processes with semi-analytical priors[J]. SSRN Electronic Journal, 2024. doi: 10.2139/ssrn.4966271

LI S W, LI S C, LAIMA S J, et al. Data-driven modeling of bridge buffeting in the time domain using long short-term memory network based on structural health monitoring[J]. Structural Control and Health Monitoring, 2021, 28(8): e2772. doi: 10.1002/stc.2772

LAIMA S J, FENG H, LI H, et al. A buffeting-net for buffeting response prediction of full-scale bridges[J]. Engineering Structures, 2023, 275: 115289. doi: 10.1016/j.engstruct.2022.115289

PETERSEN Ø W, O Ø, LOURENS E. Wind load estimation and virtual sensing in long-span suspension bridges using physics-informed Gaussian process latent force models[J]. Mechanical Systems and Signal Processing, 2022, 170: 108742. doi: 10.1016/j.ymssp.2021.108742

ALI K. New mathematical formulation of nonlinear unsteady wind loads on long-span bridge decks under nonstationary winds using time-delay neural network[J]. Journal of Structural Engineering, 2022, 148(10): 06022003. doi: 10.1061/(asce)st.1943-541x.0003476

TONDO G R, KAVRAKOV I, MORGENTHAL G. Bayesian non-linear load estimation in long-span bridges via physics-informed Gaussian processes[M]// JENSEN J S, FRANGOPOL D M, SCHMIDT J W. Bridge Maintenance, Safety, Management, Digitalization and Sustainability. CRC Press, 2024: 3522−3529. doi: 10.1201/9781003483755

ENTEZAMI A, SARMADI H. Machine learning-aided prediction of windstorm-induced vibration responses of long-span suspension bridges[J]. Computer-Aided Civil and Infrastructure Engineering, 2025, 40(8): 1043−1060. doi: 10.1111/mice.13387[LinkOut

ALAM K M R, SIDDIQUE N, ADELI H. A dynamic ensemble learning algorithm for neural networks[J]. Neural Computing and Applications, 2020, 32(12): 8675−8690. doi: 10.1007/s00521-019-04359-7

MAKKE N, CHAWLA S. Interpretable scientific discovery with symbolic regression: A review[J]. Artificial Intelligence Review, 2024, 57(1): 2. doi: 10.1007/s10462-023-10622-0

TIBSHIRANI R. Regression shrinkage and selection via the lasso[J]. Journal of the Royal Statistical Society: Series B (Methodological), 1996, 58(1): 267−288. doi: 10.1111/j.2517-6161.1996.tb02080.x

THALER S, PAEHLER L, ADAMS N A. Sparse identification of truncation errors[J]. Journal of Computational Physics, 2019, 397: 108851. doi: 10.1016/j.jcp.2019.07.049

SCHMELZER M, DWIGHT R P, CINNELLA P. Discovery of algebraic Reynolds-stress models using sparse symbolic regression[J]. Flow, Turbulence and Combustion, 2020, 104(2): 579−603. doi: 10.1007/s10494-019-00089-x

SCHMIDT M, LIPSON H. Distilling free-form natural laws from experimental data[J]. Science, 2009, 324(5923): 81−85. doi: 10.1126/science.1165893

BONGARD J, LIPSON H. Automated reverse engineering of nonlinear dynamical systems[J]. Proceedings of the National Academy of Sciences of the United States of America, 2007, 104(24): 9943−9948. doi: 10.1073/pnas.0609476104

VADDIREDDY H, RASHEED A, STAPLES A E, et al. Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data[J]. Physics of Fluids, 2020, 32(1): 015113. doi: 10.1063/1.5136351

LONG Z C, LU Y P, DONG B. PDE-Net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network[J]. Journal of Computational Physics, 2019, 399: 108925. doi: 10.1016/j.jcp.2019.108925

KIM S, LU P Y, MUKHERJEE S, et al. Integration of neural network-based symbolic regression in deep learning for scientific discovery[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(9): 4166−4177. doi: 10.1109/TNNLS.2020.3017010

D'ASCOLI S, KAMIENNY P A, LAMPLE G, et al. Deep symbolic regression for recurrence prediction[C]// 39th International Conference on Machine Learning, PMLR 162: 4520−4536, 2022.

KAMIENNY P A, LAMPLE G, LAMPRIER S, et al. Deep generative symbolic regression with monte-carlo-tree-search[C]// 40th International Conference on Machine Learning (ICML’23), 2023: 15655−15668.

ANGELIS D, SOFOS F, KARAKASIDIS T. Artificial intelligence in physical sciences: Symbolic regression trends and perspectives[J]. Archives of Computational Methods in Engineering. 2023, 30: 3845−3865. doi: 10.1007/s11831-023-09922-z

CRANMER M, SANCHEZ-GONZALEZ A, BATTAGLIA P, et al. Discovering symbolic models from deep learning with inductive biases[J]. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada. 2020.

LAIMA S J, ZHANG Z Y, JIN X W, et al. Identification of the form of self-excited aerodynamic force of bridge deck based on machine learning[J]. Physics of Fluids, 2024, 36(1): 017114. doi: 10.1063/5.0176875

MA T, CUI W, GAO T T, et al. Data-based autonomously discovering method for nonlinear aerodynamic force of quasi-flat plate[J]. Physics of Fluids, 2023, 35(2): 024103. doi: 10.1063/5.0133526

AMICO E, CAFIERO G, IUSO G. Deep reinforcement learning for active control of a three-dimensional bluff body wake[J]. Physics of Fluids, 2022, 34(10): 105126. doi: 10.1063/5.0108387

VARELA P, SUÁREZ P, ALCÁNTARA-ÁVILA F, et al. Deep reinforcement learning for flow control exploits different physics for increasing Reynolds number regimes[J]. Actuators, 2022, 11(12): 359. doi: 10.3390/act11120359

ZHENG C D, JI T W, XIE F F, et al. From active learning to deep reinforcement learning: Intelligent active flow control in suppressing vortex-induced vibration[J]. Physics of Fluids, 2021, 33(6): 063607. doi: 10.1063/5.0052524

CHEN H, GAO C Q, WU J F, et al. Study on optimization design of airfoil transonic buffet with reinforcement learning method[J]. Aerospace, 2023, 10(5): 486. doi: 10.3390/aerospace10050486

MNIH V, KAVUKCUOGLU K, SILVER D, et al. Human-level control through deep reinforcement learning[J]. Nature, 2015, 518(7540): 529−533. doi: 10.1038/nature14236

REN K, GAO C Q, XIONG N, et al. Adaptive control of transonic buffet and buffeting flow with deep reinforcement learning[J]. Physics of Fluids, 2024, 36(1): 016143. doi: 10.1063/5.0189662

PARIS R, BENEDDINE S, DANDOIS J. Robust flow control and optimal sensor placement using deep reinforcement learning[J]. Journal of Fluid Mechanics, 2021, 913: A25. doi: 10.1017/jfm.2020.1170

RABAULT J, KUCHTA M, JENSEN A, et al. Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control[J]. Journal of Fluid Mechanics, 2019, 865: 281−302. doi: 10.1017/jfm.2019.62

TANG H W, RABAULT J, KUHNLE A, et al. Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning[J]. Physics of Fluids, 2020, 32(5): 053605. doi: 10.1063/5.0006492

REN F, RABAULT J, TANG H. Applying deep reinforcement learning to active flow control in weakly turbulent conditions[J]. Physics of Fluids, 2021, 33(3): 037121. doi: 10.1063/5.0037371

FAN D X, YANG L, WANG Z C, et al. Reinforcement learning for bluff body active flow control in experiments and simulations[J]. Proceedings of the National Academy of Sciences of the United States of America, 2020, 117(42): 26091−26098. doi: 10.1073/pnas.2004939117

DENG X L, HU G, CHEN W L. Intelligent active flow control of long-span bridge deck using deep reinforcement learning integrated transfer learning[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2024, 244: 105632. doi: 10.1016/j.jweia.2023.105632

CHEN W J, WANG Q L, YAN L, et al. Deep reinforcement learning-based active flow control of vortex-induced vibration of a square cylinder[J]. Physics of Fluids, 2023, 35(5): 053610. doi: 10.1063/5.0152777

DENG X L, HU G, CHEN W L. Deep reinforcement learning based intelligent flow control of vortex-induced vibration for box girder[J]. Acta Aerodynamica Sinica, 2025, 43(5): 1−10. doi: 10.7638/kqdlxxb-2025.0016

ILBEIGI S, BAGHERZADEH A, SHARIFI A. Applied neural network-based active control for vortex-induced vibrations suppression in a two-degree-of-freedom cylinder[J]. 2025, 37(1): 017150. doi: 10.1063/5.0251228

WU T, HE J C, LI S P. Active flutter control of long-span bridges via deep reinforcement learning: A proof of concept[J]. Wind and Structures. 2023, 36(5): 321−331. doi: 10.12989/was.2023.36.5.321

HE X J, WANG Y Z, HUA Y, et al. Policy transfer of reinforcement learning-based flow control: From two- to three-dimensional environment[J]. Physics of Fluids, 2023, 35(5): 055116. doi: 10.1063/5.0147190

RABAULT J, KUHNLE A. Accelerating deep reinforcement learning strategies of flow control through a multi-environment approach[J]. Physics of Fluids, 2019, 31(9): 094105. doi: 10.1063/1.5116415

MAO Y Q, ZHONG S, YIN H J. Active flow control using deep reinforcement learning with time delays in Markov decision process and autoregressive policy[J]. Physics of Fluids, 2022, 34(5): 053602. doi: 10.1063/5.0086871

DONG X H, HONG H, DENG X L, et al. Surrogate model-based deep reinforcement learning for experimental study of active flow control of circular cylinder[J]. Physics of Fluids, 2023, 35(10): 105147. doi: 10.1063/5.0170316

KALLA D, SMITH N, KURAKU S, et al. Study and analysis of chat GPT and its impact on different fields of study[J]. International journal of innovative science and research technology, 2023, 8(3): 827−833.

PROTTASHA N J, SAMI A A, KOWSHER M, et al. Transfer learning for sentiment analysis using BERT based supervised fine-tuning[J]. Sensors, 2022, 22(11): 4157. doi: 10.3390/s22114157

LIU Z H, WANG Y H, HAN K, et al. Post-training quantization for vision transformer[C]// Advances in Neural Information Processing Systems. 2021, 34: 28092−28103.

LEWIS PATRICK, PEREZ ETHAN, PIKTUS ALEKSANDRA, et al. Retrieval-augmented generation for knowledge-intensive NLP tasks[C]// Advances in neural information processing systems. 2020, 33: 9459−9474.

TAPEH A T G, NASER M Z. Artificial intelligence, machine learning, and deep learning in structural engineering: A scientometrics review of trends and best practices[J]. Archives of Computational Methods in Engineering, 2023, 30(1): 115-159. doi: 10.1007/s11831-022-09793-w

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Fluid-structure interaction characteristics of butterfly flexible flapping wings in the forward flight

Rapid design method of rocket aerodynamic shape based on conditional diffusion model

Research on the aerodynamic performance of compressor blades considering fluid-structure interaction effects

Research progress and considerations on short-term prediction of extreme wind fields based on machine learning

Fluid-structure interaction simulation of parachute inflation based on an immersed boundary method and large eddy simulation

Related Author

Zhu Hanxi

Guo Chengpeng

Zhang Tiejun

Zhang Yang

Li Chuan

Chen Jiandong

He Lei

Cheng Ming

Related Institution

AVIC Aerodynamics Research Institute, Shenyang

College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing

State Key Laboratory of Aerodynamics, Mianyang

Computational Aerodynamics Institute of China Aerodynamics Research and Development Center, Mianyang

College of Computer Science, Sichuan University, Chengdu

AI问答

⁰