Applications of machine learning to wind engineering

T Wu, R Snaiki - Frontiers in Built Environment, 2022 - frontiersin.org
Advances of the analytical, numerical, experimental and field-measurement approaches in
wind engineering offers unprecedented volume of data that, together with rapidly evolving …

[HTML][HTML] Machine learning for bridge wind engineering

Z Zhang, S Li, H Feng, X Zhou, N Xu, H Li… - Advances in Wind …, 2024 - Elsevier
Modeling and control are primary domains in bridge wind engineering. The natural wind
field characteristics (eg, non-stationary, non-uniform, spatial-temporal changing …

Enhancing wind performance of tall buildings using corner aerodynamic optimization

A Elshaer, G Bitsuamlak, A El Damatty - Engineering Structures, 2017 - Elsevier
Wind-induced loads and motions of tall buildings usually govern the design of the lateral
load resisting systems. The outer shape of the building is one of the many parameters that …

Data-driven prediction of critical flutter velocity of long-span suspension bridges using a probabilistic machine learning approach

S Tinmitondé, X He, L Yan, AH Hounye - Computers & Structures, 2023 - Elsevier
Among the consequences of wind-induced excitation on long-span cable-supported
bridges, flutter instability is the most dangerous and can collapse bridge structures. Until …

Prediction of aeroelastic response of bridge decks using artificial neural networks

T Abbas, I Kavrakov, G Morgenthal, T Lahmer - Computers & Structures, 2020 - Elsevier
The assessment of wind-induced vibrations is considered vital for the design of long-span
bridges. The aim of this research is to develop a methodological framework for robust and …

Optimizing lift-up design to maximize pedestrian wind and thermal comfort in 'hot-calm'and 'cold-windy'climates

AU Weerasuriya, X Zhang, B Lu, KT Tse… - Sustainable Cities and …, 2020 - Elsevier
A novel building design—the lift-up design—has shown promise in removing obstacles and
facilitating wind circulation at lower heights in built-up areas, yet little is understood about …

Machine learning strategy for predicting flutter performance of streamlined box girders

H Liao, H Mei, G Hu, B Wu, Q Wang - Journal of Wind Engineering and …, 2021 - Elsevier
Engineers often heavily rely on wind tunnel tests or computational fluid dynamics (CFD) to
evaluate the flutter performance of bridges in their preliminary design, which is costly and …

Prediction of solitary wave forces on coastal bridge decks using artificial neural networks

G Xu, Q Chen, J Chen - Journal of Bridge Engineering, 2018 - ascelibrary.org
This study proposes an alternative and competitive methodology for predicting solitary wave
forces on coastal bridge decks using artificial neural networks (ANNs). It is imperative to …

[HTML][HTML] A Gaussian Process-Based emulator for modeling pedestrian-level wind field

AU Weerasuriya, X Zhang, B Lu, KT Tse… - Building and …, 2021 - Elsevier
Wind tunnel tests and computational fluid dynamics (CFD) simulations remain the main
modeling techniques in wind engineering despite being expensive, time-consuming, and …

Simulation of unsteady flow around bluff bodies using knowledge-enhanced convolutional neural network

X Yu, T Wu - Journal of Wind Engineering and Industrial …, 2023 - Elsevier
The unsteady flow with massive separation poses challenges to accurately and efficiently
simulate wind effects on civil structures, especially in the search for optimal aerodynamic …