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CONTENTS
Volume 36, Number 6, June 2023 (Special Issue)
 

Abstract
Although machine learning (ML) techniques have been widely used in various fields of engineering practice, their applications in the field of wind engineering are still at the initial stage. In order to evaluate the feasibility of machine learning algorithms for prediction of wind loads on high-rise buildings, this study took the exposure category type, wind direction and the height of local wind force as the input features and adopted four different machine learning algorithms including k-nearest neighbor (KNN), support vector machine (SVM), gradient boosting regression tree (GBRT) and extreme gradient (XG) boosting to predict wind force coefficients of CAARC standard tall building model. All the hyper-parameters of four ML algorithms are optimized by tree-structured Parzen estimator (TPE). The result shows that mean drag force coefficients and RMS lift force coefficients can be well predicted by the GBRT algorithm model while the RMS drag force coefficients can be forecasted preferably by the XG boosting algorithm model. The proposed machine learning based algorithms for wind loads prediction can be an alternative of traditional wind tunnel tests and computational fluid dynamic simulations.

Key Words
high-rise building; hyper-parameters optimization; machine learning; predicting; wind load

Address
Yi Li:1)School of Civil Engineering, Changsha University of Science and Technology, Changsha, 410114, Hunan, China
2)School of Civil Engineering, Hunan University of Science and Technology, Xiangtan, 411201, Hunan, China

Jie-Ting Yin:School of Civil Engineering, Hunan University of Science and Technology, Xiangtan, 411201, Hunan, China

Fu-Bin Chen:1)School of Civil Engineering, Changsha University of Science and Technology, Changsha, 410114, Hunan, China 2)Key Laboratory of Bridge Engineering Safety Control by Department of Education, Changsha University of Science and Technology,
Changsha, 410114, Hunan, China

Qiu-Sheng Li:Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong

Abstract
This study proposes a few-shot learning model for extrapolating the wind pressure of scaled experiments to fullscale measurements. The proposed ML model can use scaled experimental data and a few full-scale tests to accurately predict the remaining full-scale data points (for new specimens). This model focuses on extrapolating the prediction to different scales while existing approaches are not capable of accurately extrapolating from scaled data to full-scale data in the wind engineering domain. Also, the scaling issue observed in wind tunnel tests can be partially resolved via the proposed approach. The proposed model obtained a low mean-squared error and a high coefficient of determination for the mean and standard deviation wind pressure coefficients of the full-scale dataset. A parametric study is carried out to investigate the influence of the number of selected shots. This technique is the first of its kind as it is the first time an ML model has been used in the wind engineering field to deal with extrapolation in wind performance prediction. With the advantages of the few-shot learning model, physical wind tunnel experiments can be reduced to a great extent. The few-shot learning model yields a robust, efficient, and accurate alternative to extrapolating the prediction performance of structures from various model scales to full-scale.

Key Words
few-shot learning; full-scale measurement; machine learning; multiple-scale extrapolation; wind pressure coefficients

Address
Yanmo Weng and Stephanie G. Paal:Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843, United States

Abstract
Structural health monitoring is used to ensure the well-being of civil structures by detecting damage and estimating deterioration. Wind flow applies external loads to high-rise buildings, with the horizontal force component of the wind causing structural displacements in high-rise buildings. This study proposes a deep learning-based predictive model for measuring lateral displacement response in high-rise buildings. The proposed long short-term memory model functions as a sequence generator to generate displacements on building floors depending on the displacement statistics collected on the top floor. The model was trained with wind-induced displacement data for the top floor of a high-rise building as input. The outcomes demonstrate that the model can forecast wind-induced displacement on the remaining floors of a building. Further, displacement was predicted for each floor of the high-rise buildings at wind flow angles of 0° and 45°. The proposed model accurately predicted a high-rise building model's story drift and lateral displacement. The outcomes of this proposed work are anticipated to serve as a guide for assessing the overall lateral displacement of high-rise buildings.

Key Words
high-rise buildings; long short-term memory; recurrent neural network; structural health monitoring; windinduced displacement

Address
Bubryur Kim:Department of Robot and Smart System Engineering, Kyungpook National University,
80, Daehak-ro, Buk-gu, Daegu, 41566, Republic of Korea

K.R. Sri Preethaa:Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore – 641407, India

Zengshun Chen:School of Civil Engineering, Chongqing University, Chongqing 400045, China

Yuvaraj Natarajan:1)Department of Robot and Smart System Engineering, Kyungpook National University,
80, Daehak-ro, Buk-gu, Daegu, 41566, Republic of Korea
2)Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore – 641407, India

Gitanjali Wadhwa:Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore – 641407, India

Hong Min Lee:Engineering Co., Ltd., 128, Beobwon-ro, Songpa-gu, Seoul, Republic of Korea

Abstract
Skewness and kurtosis are important higher-order statistics for simulating non-Gaussian wind pressure series on low-rise buildings, but their predictions are less studied in comparison with those of the low order statistics as mean and rms. The distribution gradients of skewness and kurtosis on roofs are evidently higher than those of mean and rms, which increases their prediction difficulty. The conventional artificial neural networks (ANNs) used for predicting mean and rms show unsatisfactory accuracy in predicting skewness and kurtosis owing to the limited capacity of shallow learning of ANNs. In this work, the deep neural networks (DNNs) model with the ability of deep learning is introduced to predict the skewness and kurtosis on a low-rise building. For obtaining the optimal generalization of the DNNs model, the hyper parameters are automatically determined by Bayesian Optimization (BO). Moreover, for providing a benchmark for future studies on predicting higher order statistics, the data sets for training and testing the DNNs model are extracted from the internationally open NISTUWO database, and the prediction errors of all taps are comprehensively quantified by various error metrices. The results show that the prediction accuracy in this study is apparently better than that in the literature, since the correlation coefficient between the predicted and experimental results is 0.99 and 0.75 in this paper and the literature respectively. In the untrained cornering wind direction, the distributions of skewness and kurtosis are well captured by DNNs on the whole building including the roof corner with strong non-normality, and the correlation coefficients between the predicted and experimental results are 0.99 and 0.95 for skewness and kurtosis respectively.

Key Words
aerodynamic database; deep learning; kurtosis; low-rise buildings; skewness; wind pressure coefficients

Address
Youqin Huang, Guanheng Ou, Jiyang Fu and Huifan Wu:Research Centre for Wind Engineering and Engineering Vibration, Guangzhou University,
230 West Waihuan Road, Higher Education Mega Center, Guangzhou, China
:

Abstract
Strong wind is the main factors of wind-damage of high-rise buildings, which often creates largely economical losses and casualties. Wind pressure plays a critical role in wind effects on buildings. To obtain the high-resolution wind pressure field, it often requires massive pressure taps. In this study, two traditional methods, including bilinear and bicubic interpolation, and two deep learning techniques including Residual Networks (ResNet) and Generative Adversarial Networks (GANs), are employed to reconstruct wind pressure filed from limited pressure taps on the surface of an ideal building from TPU database. It was found that the GANs model exhibits the best performance in reconstructing the wind pressure field. Meanwhile, it was confirmed that k-means clustering based retained pressure taps as model input can significantly improve the reconstruction ability of GANs model. Finally, the generalization ability of k-means clustering based GANs model in reconstructing wind pressure field is verified by an actual engineering structure. Importantly, the k-means clustering based GANs model can achieve satisfactory reconstruction in wind pressure field under the inputs processing by k-means clustering, even the 20% of pressure taps. Therefore, it is expected to save a huge number of pressure taps under the field reconstruction and achieve timely and accurately reconstruction of wind pressure field under k-means clustering based GANs model.

Key Words
buildings; deep learning; generative adversarial networks; super resolution; wind pressure

Address
Xiao Chen, Xinhui Dong and Pengfei Lin: Artificial Intelligence for Wind Engineering (AIWE) Lab, School of Civil and Environmental Engineering,
Harbin Institute of Technology, Shenzhen 518055, China

Fei Ding:NatHaz Modeling Laboratory, University of Notre Dame, Notre Dame, IN 46556, USA

Bubryur Kim:Department of Robot and Smart System Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, Korea

Jie Song:Research Center of Urban Disasters Prevention and Fire Rescue Technology of Hubei Province, School of Civil Engineering,
Wuhan University, Wuhan, China

Yiqing Xiao and Gang Hu:1)Artificial Intelligence for Wind Engineering (AIWE) Lab, School of Civil and Environmental Engineering,
Harbin Institute of Technology, Shenzhen 518055, China
2)Guangdong Provincial Key Laboratory of Intelligent and Resilient Structures for Civil Engineering,
Harbin Institute of Technology, Shenzhen, 518055, China
3)Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications,
Harbin Institute of Technology, Shenzhen, 518055, China


Abstract
Aerodynamic force coefficients are generally obtained from traditional wind tunnel tests or computational fluid dynamics (CFD). Unfortunately, the techniques mentioned above can sometimes be cumbersome because of the cost involved, such as the computational cost and the use of heavy equipment, to name only two examples. This study proposed to build a deep neural network model to predict the aerodynamic force coefficients based on data collected from CFD simulations to overcome these drawbacks. Therefore, a series of CFD simulations were conducted using different geometric parameters to obtain the aerodynamic force coefficients, validated with wind tunnel tests. The results obtained from CFD simulations were used to create a dataset to train a multilayer perceptron artificial neural network (ANN) model. The models were obtained using three optimization algorithms: scaled conjugate gradient (SCG), Bayesian regularization (BR), and Levenberg-Marquardt algorithms (LM). Furthermore, the performance of each neural network was verified using two performance metrics, including the mean square error and the R-squared coefficient of determination. Finally, the ANN model proved to be highly accurate in predicting the force coefficients of similar bridge sections, thus circumventing the computational burden associated with CFD simulation and the cost of traditional wind tunnel tests.

Key Words
aerodynamic coefficients; artificial neural network; computational fluid dynamics; long-span bridges; optimization, accuracy

Address
Severin Tinmitonde, Xuhui He and Lei Yan:1)National Engineering Research Center of High-speed Railway Construction Technology, Central South University, Changsha, China
2)School of Civil Engineering, Central South University, Changsha, China
3)Hunan Provincial Key Laboratory for Disaster Prevention and Mitigation of Rail Transit Engineering Structures, Changsha, China

Cunming Ma:Department of Bridge Engineering, Southwest Jiaotong University, Chengdu, China

Haizhu Xiao:Major Bridge Reconnaissance & Design Institute Co., Ltd., Wuhan, China

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