# Machine Learning Techniques in Structural Wind Engineering: A State-of-the-Art Review
###### Abstract
[15] presented a novel approach for detecting the areas that are affected by flooding through the integration of ML and image processing. Moreover, ML applications were implemented in many other fields related to civil engineering generally and structural engineering particularly [16; 17; 18; 19; 20; 21; 22; 23; 24; 25], structural damage detection [26; 27; 28; 29], structural health monitoring [30; 31; 32; 33], and geotechnical engineering [34; 35; 36; 37; 38; 39]. In addition, ML techniques, such as Gaussian regression, can be used for numerical weather predictions [40]. Taking into consideration the above efforts to summarize ML techniques and their applications for different civil engineering sub-disciplines, no previous studies focused on structural wind engineering. Thus, the objective of this paper is to fill this important knowledge gap by providing a thorough and comprehensive review of ML techniques and implementations in structural wind engineering. To better relate ML implementations, a brief overview of typical structural wind engineering problems is provided first. Bluff body aerodynamics is associated with a high level of complexity due to the several ways that wind flow interacts with civil engineering structures. Wind flow at the bottom of the atmosphere is influenced by the roughness of the natural terrain as well as by the built environment itself. As a result, eddies are formed that vary in size and shape and travel with the wind creating the well-known atmospheric boundary layer (ABL) flow characteristics [41]. Studying and understanding the behavior of wind and its interaction with buildings and other structures is critical in the analysis and design process. Generally, ABL wind tunnel testing is still the most reliable tool to assess the aerodynamics of any structure and provide an accurate surface pressure and/or aeroelastic response. Computational fluid dynamics (CFD) tools became more popular and can perform well in predicting mostly mean, and in some cases peak, wind flow characteristics and corresponding loads on structures. To address larger problems, ML techniques were recently introduced in different applications in wind engineering but mostly to support and expand experimental and numerical wind engineering studies. Based on the above introduction and the witnessed increased interest to incorporate ML techniques in structural wind engineering, a state-of-the-art review of the existing literature is beneficial and timely, which motivates this study. The goal of this paper again is to present an overview of the state of knowledge for commonly used ML methods in structural wind engineering as well as try to identify prospective research domains. We focus on the different ML methods that were used mainly for predicting wind-induced loads or aeroelastic responses. Therefore, eight major ML methods that were commonly used in the previous studies are the core of this review. These are: (1) artificial neural networks (ANN), (2) decision tree regression (DT), (3) ensemble methods (EM) that include: random forest (RF), gradient boosting regression tree (GBRT) or alternatively referred to as gradient boosting decision tree (GBDT), and XGboost, (4) fuzzy neural networks (FNN), (5) the gaussian regression process (GRP), (6) generative adversarial networks (GAN), (7) k-nearest neighbor regression (KNN), and (8) support vector regression (SVR). The review and discussion following this introduction are divided into four sections. The first section goes over the different ML methods that were previously used through an overview of the formulation and the theoretical background for each method. This is to provide a fair context before discussing their applications for prediction and classification purposes.