Machine Learning Techniques in Structural Wind Engineering: A State-of-the-Art Review

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 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.

Keywords

• machine learning
• neural networks
• wind engineering
• wind-induced pressure
• aeroelastic response
• computational fluid dynamics