# Machine Learning Techniques in Structural Wind Engineering: A State-of-the-Art Review
## 3 Prior Studies on Applying ML Techniques in Structural Wind Engineering
### Aeroelastic Response Prediction Using ML
[PERSON] and [PERSON] [136] investigated the vortex-induced vibrations (VIV) of two circular cylinders with the same dimensions but staggered configurations, using three ML algorithms: DT, RF, and GBRT. The two cylinders were modeled first into a CFD simulation, and the mass ratio, wind direction, the distance between cylinders, and wind velocity were used as input variables. The GBRT algorithm was the most accurate in predicting the amplitude of the upstream and downstream vibration. [PERSON] et al. [132] employed ANN to predict the aeroelastic response of bridge decks using response time histories as the input variables. The predicted forces were compared with CFD findings to evaluate the ANN model. The ANN model was also coupled with the structural model to determine the aeroelastic instability limit of the bridge section, which demonstrated the potential use of this framework to predict the aeroelastic response for other bridge cross-sections. More recently, surrogate models have been used widely in different areas related to structural wind engineering [149; 150; 151; 152]. One type of surrogate model is using the aid of finite element models (FEM) to obtain an output that can be used as an input in the trained model of the ML. [PERSON] et al. [153] used a surrogate model in which the ANN was applied to the FE model to update the model parameter for computing the dynamic response of a cable-suspended roof while using the wind loads from full-scale measurements for three typhoon events in three consecutive years from 2011 to 2014. [PERSON] and [PERSON] [154] proposed a surrogate model using a convolutional neural network (CNN) for systems with high dimensional inputs/outputs. [PERSON] and [PERSON] [145] predicted the wind-induced vertical displacement of a cable net roof using ANN. The trained model used wind tunnel pressure coefficient datasets and FEM wind-induced vertical displacement datasets. The surrogate model showed that it can successfully replicate more complex geometrically nonlinear structural behavior. [PERSON] and [PERSON] [155] used surrogate flutter derivate models to predict the flutter velocity of a suspension bridge. The ANN model was trained using the critical flutter velocities dataset by calculating the flutter derivatives experimentally. The model successfully generated a large dataset of critical flutter velocities. In addition, surrogate modeling could analyze the structural performance of vertical structures under tornado loads by training fragilities using ANN [156; 157]. [PERSON] et al. [146] used a light gradient boosting machine (LGBM) method, which is an optimized version of the GBDT algorithm proposed by [PERSON] et al. [158], with a clustering algorithm to predict the crosswind force spectra of tall buildings. This optimized algorithm combined two techniques in training the models: the gradient base one side sampling (GOSS) and the exclusive feature bundling (EFB). The results showed that the proposed method is effective and efficient to predict the crosswind force spectrum for a rectangular tall building. [PERSON] et al. [143] used four different ML techniques (i.e., SVR, ANN, RF, and GBRT) to predict the flutter wind speed for a box girder bridge. The ANN and GBRT models accurately predicted the flutter wind speed for the streamlined box girders. The buffeting response of bridges can be predicted analytically using buffeting theory. However, some previous studies [159; 160; 161; 162; 163] have shown inconsistency between full-scale measured response and buffeting theory estimates. Thus, [PERSON] et al. [142] trained two ML models (ANN and SVR) to estimate the buffeting response speed using full-scale data from the Hardanger bridge in Norway. The two ML models predicted the bridge response more accurately than the buffeting theory when compared to the full-scale measurement.