# 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. machine learning: neural networks; wind engineering; wind-induced pressure; aeroelastic response; computational fluid dynamics +
Footnote †: journal: Applied  
(i.e., clustering and dimensionality reduction). In semi-supervised learning, the computer is trained with a mixture of labeled and unlabeled sets. In reinforcement learning, which is so far the least common learning type, the computer acquires knowledge by observing the data through some iterations that require reinforcement signals to identify the predictive behavior or action (i.e., make decisions) [3; 7]. ML is becoming more prevalent in civil engineering with numerous studies publishing reviews and applications of ML in this field. While this paper focuses only on structural wind applications as explained later, a few key general summary studies or reviews are listed first for the convenience of the readers interested in broader applications. [PERSON] in [8] reviewed the applications of artificial neural networks (ANN) in the fields of structural engineering and construction management. The study presented the integration of neural networks with different computing paradigms (i.e., fuzzy logic, genetic algorithm, etc.). [PERSON] et al. [9] reviewed different studies on the support vector machine (SVM) method in structural engineering and studied the feasibility of this approach by providing three case studies. Similarly, [PERSON] et al. [10] investigated the usability of SVM for classification and regression problems using data for horizontal force initiated by dynamic waves on a vertical structure. Recently, [PERSON] et al. [4] presented a review of historical and recent developments of ML applications in the area of structural design and performance assessment for buildings. More recently, ML applications have been involved in predicting catastrophic natural hazards. Recent studies investigated the integration of real-time hybrid simulation (RTHS) with deep learning (DL) algorithms to represent the dynamic behavior of nonlinear analytical substructures [11; 12]. A comprehensive review was also provided by [PERSON] et al. [13] on the progress and challenges of ML applications in the field of earthquake engineering including seismic hazard analysis and seismic fragility. [PERSON] et al. [14] demonstrated state-of-the-art ML methods for flood prediction and the most promising methods to predict long- and short-term floods. Likewise, [PERSON] et al.