Advances in Structural Wind-Resistant Analysis and Disaster Mitigation

Topic Information

Dear Colleagues,

Given the rapid pace of climate change, there has been an increasing frequency of extreme wind events such as typhoons/hurricanes, downbursts, tornadoes, and severe mountainous winds. These extreme winds often lead to severe structural vibrations or failures in a wide range of engineering structures. Under these conditions, the high-fidelity analysis of structural wind effects via theoretical analysis, numerical simulation, wind tunnel test, and field measurement becomes a critical issue in the field of structural wind engineering, and wind disaster mitigation has attracted a great deal of attention in engineering communities. In this regard, this topic is devoted to sharing recent advances in structural wind-resistant analysis and disaster mitigation. Topics of interest include (but are not limited to) the following:

• Wind field analysis and simulation of extreme winds;
• Modelling of structural aerodynamics;
• Structural reliability analysis exposed to wind disasters;
• Uncertainty quantification of wind-induced stochastic dynamics;
• Experiments of extreme wind effects on structures;
• AI-based approaches for structural wind-resistant analysis;
• Prediction of wind speeds and structural wind effects;
• Mitigation of wind-induced vibrations.

Dr. Tianyou Tao
Dr. Haojun Tang
Dr. Fei Ding
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400	Submit
Energies energies	3.0	6.2	2008	16.8 Days	CHF 2600	Submit
Journal of Marine Science and Engineering jmse	2.7	4.4	2013	16.4 Days	CHF 2600	Submit
Sustainability sustainability	3.3	6.8	2009	19.7 Days	CHF 2400	Submit
Wind wind	-	-	2021	35.7 Days	CHF 1000	Submit

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Published Papers (4 papers)

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All Applied Sciences Energies JMSE Sustainability Wind

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25 pages, 30150 KiB  

Open AccessArticle

Vortex-Induced Vibration Performance Prediction of Double-Deck Steel Truss Bridge Based on Improved Machine Learning Algorithm

by Yang Yang, Huiwen Hou, Gang Yao and Bo Wu

J. Mar. Sci. Eng. 2025, 13(4), 767; https://doi.org/10.3390/jmse13040767 - 12 Apr 2025

Viewed by 194

Abstract

The span of a double-deck cross-sea bridge that can be used for both highway and railway purposes is usually 1 to 16 km. Compared with small-span bridges and single-layer main girder forms, its lightweight design and low damping characteristics make it more prone [...] Read more.

The span of a double-deck cross-sea bridge that can be used for both highway and railway purposes is usually 1 to 16 km. Compared with small-span bridges and single-layer main girder forms, its lightweight design and low damping characteristics make it more prone to vortex-induced vibration (VIV). To predict the VIV performance of a double-deck steel truss (DDST) girder with additional aerodynamic measures, the VIV response of a DDST bridge was investigated using wind tunnel tests and numerical simulation, a learning sample database was established with numerical simulation results, and a prediction model for the amplitude of the DDST girder and VIV parameters was established based on three machine learning algorithms. The optimization algorithm was selected using root mean square error (RMSE) and the coefficient of determination (R²) as evaluation indices and further improved with a genetic algorithm and particle swarm optimization. The results show that for the amplitude prediction of the main girder, the backpropagation neural network model is the most effective. The most improved algorithm yields an RMSE of 0.150 and an R² of 0.9898. For the prediction of VIV parameters, the Random Forest model is the most effective. The RMSE values of the improved optimal algorithm are 0.017, 0.026, and 0.295, and the R² values are 0.9421, 0.8875, and 0.9462. The prediction model is more efficient in terms of computational efficiency compared to the numerical simulation method. Full article

(This article belongs to the Topic Advances in Structural Wind-Resistant Analysis and Disaster Mitigation)

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18 pages, 12981 KiB  

Open AccessArticle

Galloping Performance of Transmission Line System Aeroelastic Model with Rime Through Wind-Tunnel Tests

by Mingguan Zhao, Meng Li, Shenglong Li, Yuanhao Wan, Yang Hai and Chunguang Li

Energies 2025, 18(5), 1203; https://doi.org/10.3390/en18051203 - 28 Feb 2025

Cited by 1 | Viewed by 439

Abstract

This study presents an experimental investigation for the galloping performance of the transmission line system with rime under wind excitation. A full aeroelastic model wind-tunnel test is conducted to investigate the dynamic response of a two-bundled transmission line system with rime under different [...] Read more.

This study presents an experimental investigation for the galloping performance of the transmission line system with rime under wind excitation. A full aeroelastic model wind-tunnel test is conducted to investigate the dynamic response of a two-bundled transmission line system with rime under different conditions. The time histories of the displacement of the conductor and the acceleration of the tower are measured in detail to analyze the characteristic of the wind-induced response. A comprehensive parametric experiment is performed to explore the effects of wind speed, wind direction, the number of conductor spans and the coupling between the conductor and the tower on the galloping performance of the transmission line system with rime. The results showed that the wind speed, wind direction and the number of conductor spans have significant influence on the galloping performance of conductor. The zero-degree wind direction is the most dangerous direction for the conductor. The multi-span conductor has different galloping initiation wind speed and vibration characteristics compared to the single-span conductor. The coupling effect between the conductor and the tower has trivial influence on the response of tower. This study uses 3D-printing models to simulate the aerodynamic shape of ice-covered wires with different thicknesses for wind-tunnel tests and obtains the influence of a series of parameters on the galloping vibration of transmission tower line systems. Full article

(This article belongs to the Topic Advances in Structural Wind-Resistant Analysis and Disaster Mitigation)

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19 pages, 3944 KiB  

Open AccessArticle

Study of Reynolds Number Effects on Aerodynamic Forces and Vortex-Induced Vibration Characteristics of a Streamlined Box Girder

by Binxuan Wang, Yifei Sun, Qingkuan Liu, Zhen Li, Yuan Han and Kaiwen Li

Appl. Sci. 2025, 15(4), 2202; https://doi.org/10.3390/app15042202 - 19 Feb 2025

Viewed by 449

Abstract

Due to the limitations of wind tunnel speed and size, achieving a model’s Reynolds number equal to the actual Reynolds number is challenging and may lead to discrepancies between experimental and actual results. To investigate the effects of the Reynolds number on the [...] Read more.

Due to the limitations of wind tunnel speed and size, achieving a model’s Reynolds number equal to the actual Reynolds number is challenging and may lead to discrepancies between experimental and actual results. To investigate the effects of the Reynolds number on the aerodynamic forces and vortex-induced vibration (VIV) characteristics of a streamlined box girder, wind tunnel tests were conducted to study the variations in aerodynamic forces and surface pressures on the static main beam, as well as the VIV response and time–frequency characteristics of the aerodynamic forces on the dynamic main beam, as the Reynolds number varied. The results indicate that in static tests, as the Reynolds number increases, the drag coefficient of the main beam decreases, the lift coefficient slightly increases, and the pitching moment coefficient remains almost unchanged. The root mean square (RMS) values of the wind pressure coefficients show a significant Reynolds number effect, with values generally decreasing as the Reynolds number increases. In free vibration tests, as the Reynolds number increases, the onset wind speed of VIV increases from 14.35 m/s to 16.03 m/s, the maximum amplitude decreases from 0.076 to 0.004, and the VIV lock-in range narrows. The dynamic pressure results indicate that as the Reynolds number increases, the RMS values of the wind pressure coefficients decrease. At some measurement points, the dominant frequencies of the fluctuating pressure amplitude spectra deviate from the corresponding VIV frequency, and the correlation and contribution coefficients between the local aerodynamic forces and the overall vortex-induced force (VIF) decrease. These changes may explain the reduction in the VIV amplitude with an increasing Reynolds number. The motion state of the main beam has a minimal effect on the mean wind pressure coefficients and their Reynolds number effect, whereas it has a more significant effect on the RMS values of the pressure coefficients. Full article

(This article belongs to the Topic Advances in Structural Wind-Resistant Analysis and Disaster Mitigation)

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12 pages, 3909 KiB  

Open AccessArticle

Forecasting of Tropical Storm Wind Speeds Based on Multi-Step Differencing and Artificial Neural Network

by Tianyou Tao, Peng Deng, Fan Xu and Yichao Xu

J. Mar. Sci. Eng. 2025, 13(2), 372; https://doi.org/10.3390/jmse13020372 - 17 Feb 2025

Viewed by 443

Abstract

The tropical storm is a severe wind disaster that frequently attacks coastal structures and infrastructure facilities. Accurate wind speed forecasting of tropical storms, based on real-time measured data, has become a critical issue in the engineering community. Utilizing the measured data of typical [...] Read more.

The tropical storm is a severe wind disaster that frequently attacks coastal structures and infrastructure facilities. Accurate wind speed forecasting of tropical storms, based on real-time measured data, has become a critical issue in the engineering community. Utilizing the measured data of typical tropical storms at Sutong Bridge, this study develops a new approach for wind speed forecasting, which integrates multi-step differencing with an artificial neural network-based model. Given the non-stationary nature of tropical storm wind speeds, a multi-step differencing operation is initially applied to the wind speed time series. Subsequently, multi-step predictions of the differenced wind speeds are made for future time points. Finally, an inverse differencing operation is employed to reconstruct the wind speeds to be forecasted. The forecasting errors associated with single-step differencing, multi-step differencing, and no differencing are compared to evaluate their respective performances. To validate the generalizability of the developed approach, it is further used in the wind speed forecasting of another typhoon wind speed dataset. The satisfactory performance demonstrates the effectiveness of the developed approach for multi-step wind speed forecasting of tropical storms. Full article

(This article belongs to the Topic Advances in Structural Wind-Resistant Analysis and Disaster Mitigation)

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