Special Issue "Recent Advances in Wind Engineering: Innovative Methods and Technologies"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: 20 October 2022 | Viewed by 2441

Special Issue Editors

Prof. Dr. Yong Chen
E-Mail Website
Guest Editor
College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
Interests: structural dynamics; wind engineering; steel structures
Dr. Haiwei Xu
E-Mail Website
Guest Editor
College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
Interests: wind engineering; CFD simulation; vibration suppression
Special Issues, Collections and Topics in MDPI journals
Dr. Tianyou Tao
E-Mail Website
Guest Editor
School of Civil Engineering, Southeast University, Nanjing, China
Interests: wind engineering; wind hazard modelling; bridge aerodynamics; structural health monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is devoted to the introduction and application of the latest knowledge and techniques in wind engineering. High-rise and long-span structures are usually vulnerable to strong winds. Wind-induced structural vibration, damage and even collapse have been extensively reported and have attracted wide-ranging attention from engineers and researchers. With the increase in population density in large cities and advancements in building construction achievements, the demand for super-high buildings, super-long-span bridges, large-expanse structures, etc., grows rapidly. Meanwhile, extreme wind events, e.g., tropical cyclones, tornados, storm surges, etc., have shown increasing trends in both occurrence frequency and intensity due to global climate change. These factors have brought new challenges to the wind-resistant design of buildings and structures in 21st century. Therefore, innovative methods and technologies related to theoretical analysis, numerical simulation, wind tunnel test, and field measurement have been developed to promote advances in structural aerodynamics. This Special Issue calls for papers on recent advances in wind engineering. All wind engineering communities are welcome to contribute their innovative and latest research findings to this Special Issue.

Prof. Dr. Yong Chen
Dr. Haiwei Xu
Dr. Tianyou Tao
Guest Editors

Manuscript Submission Information

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Keywords

  • extreme wind events
  • structural aerodynamics
  • wind tunnel test
  • CFD simulation
  • vibration suppression
  • wind loading
  • field measurement
  • high-rise/long-span structures

Published Papers (4 papers)

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Research

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Article
Effects of Corner Modification on the Wind-Induced Responses of High-Rise Buildings
Appl. Sci. 2022, 12(19), 9739; https://doi.org/10.3390/app12199739 - 27 Sep 2022
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Abstract
Aerodynamic optimization of building geometry has received significant attention in the design community. In this paper, a process with the high-frequency force balance (HFFB) technique to determine the most effective mitigation measure and the synchronized pressure integration (SPI) technique to verify the effect [...] Read more.
Aerodynamic optimization of building geometry has received significant attention in the design community. In this paper, a process with the high-frequency force balance (HFFB) technique to determine the most effective mitigation measure and the synchronized pressure integration (SPI) technique to verify the effect is developed for the aerodynamic optimization of high-rise buildings. Then, the process is applied to a 318 m-tall high-rise building. Tests show that the wind force on the building will not be symmetrical about the wind azimuth due to the interfering effect. The standard deviation of the base bending moment in the cross-wind direction is much larger than that in the along-wind direction. It indicates that the cross-wind loads will be dominated, providing a remarkable building height. The aerodynamic treatment of corner modifications has a considerable benefit in reducing the cross-wind loads and responses. Among the four corner modifications, the model with a 10% roundness radius to width ratio has the best mitigation effect in the along wind and cross-wind direction. Furthermore, the mean and extreme base overturning moments obtained by the SPI and the HFFB tests almost coincided with wind azimuth with acceptable discrepancy. Full article
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Article
Unfrozen Skewed Turbulence for Wind Loading on Structures
Appl. Sci. 2022, 12(19), 9537; https://doi.org/10.3390/app12199537 - 22 Sep 2022
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Abstract
The paper introduces an algorithm to generate a three-variate four-dimensional wind turbulence field suited for yawed wind dynamic load simulation. At large yaw angles, a relaxation of Taylor’s hypothesis of frozen turbulence becomes relevant as well as the flow phase lag in the [...] Read more.
The paper introduces an algorithm to generate a three-variate four-dimensional wind turbulence field suited for yawed wind dynamic load simulation. At large yaw angles, a relaxation of Taylor’s hypothesis of frozen turbulence becomes relevant as well as the flow phase lag in the along-wind direction, which modulates the real and imaginary parts of the coherence. To capture such a general wind action on a structure, a modified spectral representation method is used where the coherence of turbulence is described as a complex-valued function. The one-point and two-point co-spectra are implemented in the simulation setup using a square-root-free Cholesky decomposition of the spectral matrix. The numerical procedure is illustrated based on turbulence characteristics derived from data collected during storm Aina (2017) on the Norwegian coast by three-dimensional sonic anemometers. During this event, a remarkable 3-hour stationary time series with a mean wind speed of 24 m s1 at a height of 49 m above ground was recorded. Since no computational grid is needed, the velocity fluctuations with representative spatio-temporal characteristics can be directly simulated on structural elements of slender structures. Such an algorithm may be essential for the design of super-long span bridges in coastal areas. Full article
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Review

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Review
Machine Learning Techniques in Structural Wind Engineering: A State-of-the-Art Review
Appl. Sci. 2022, 12(10), 5232; https://doi.org/10.3390/app12105232 - 22 May 2022
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Abstract
Machine learning (ML) techniques, which are a subset of artificial intelligence (AI), have played a crucial role across a wide spectrum of disciplines, including engineering, over the last decades. The promise of using ML is due to its ability to learn from given [...] Read more.
Machine learning (ML) techniques, which are a subset of artificial intelligence (AI), have played a crucial role across a wide spectrum of disciplines, including engineering, over the last decades. The promise of using ML is due to its ability to learn from given data, identify patterns, and accordingly make decisions or predictions without being specifically programmed to do so. This paper provides a comprehensive state-of-the-art review of the implementation of ML techniques in the structural wind engineering domain and presents the most promising methods and applications in this field, such as regression trees, random forest, neural networks, etc. The existing literature was reviewed and categorized into three main traits: (1) prediction of wind-induced pressure/velocities on different structures using data from experimental studies, (2) integration of computational fluid dynamics (CFD) models with ML models for wind load prediction, and (3) assessment of the aeroelastic response of structures, such as buildings and bridges, using ML. Overall, the review identified that some of the examined studies show satisfactory and promising results in predicting wind load and aeroelastic responses while others showed less conservative results compared to the experimental data. The review demonstrates that the artificial neural network (ANN) is the most powerful tool that is widely used in wind engineering applications, but the paper still identifies other powerful ML models as well for prospective operations and future research. Full article
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Other

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Technical Note
Typhoon Loss Assessment in Rural Housing in Ningbo Based on Township-Level Resolution
Appl. Sci. 2022, 12(7), 3463; https://doi.org/10.3390/app12073463 - 29 Mar 2022
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Abstract
The purpose of this paper was to provide a new approach to achieve quantitative and accurate typhoon loss assessment of disaster-bearing bodies at township-level resolution. Based on the policy insurance data of Ningbo city, this paper took rural housing as the target disaster-bearing [...] Read more.
The purpose of this paper was to provide a new approach to achieve quantitative and accurate typhoon loss assessment of disaster-bearing bodies at township-level resolution. Based on the policy insurance data of Ningbo city, this paper took rural housing as the target disaster-bearing body and analyzed the aggregated data of disaster losses such as payout amount and insured loss rate of rural housing in Ningbo area under the influence of 25 typhoons during 2014–2019. The intensity data of disaster-causing factors such as the maximum average wind speed in Ningbo area under the influence of 25 typhoons were simulated and generated with the wind field engineering model, and a township-level high-resolution rural housing typhoon loss assessment model was established using a RBF artificial neural network. It was found that the insured loss rate of rural housing under wind damage was higher in the townships of southern Ningbo than in the townships of northern Ningbo, and the townships with larger insured loss rates were concentrated in mountainous or coastal areas that are prone to secondary disasters under the attack of the typhoon’s peripheral spiral wind and rain belt. The RBF neural network can effectively establish a typhoon loss assessment model from the causal factors to the losses of the disaster-bearing bodies, and the RBF neural network has a faster convergence speed and a smaller overall prediction error than the commonly used BP neural network. Full article
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