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Wind and Structures Volume 39, Number 6, December 2024 , pages 435-451 DOI: https://doi.org/10.12989/was.2024.39.6.435 |
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Enhanced wind velocity imputation near building structures using advanced machine learning techniques |
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Istiak Ahammed, Sujeen Song, Gang Hu, Jinwoo An and Bubryur Kim
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| Abstract | ||
| The evaluation of instantaneous wind flow patterns nearest to building architecture is crucial to ensuring structural stability, architectural integrity, and pedestrian safety. Particle image velocimetry (PIV), a technique for studying fluid flow by tracing particles, provides accurate predictions of instantaneous wind velocities (IWV). However, PIV encounters challenges in specific regions due to laser light-based experimentation, leading to missing data. Consequently, investigating the wind circulation pattern around buildings becomes more challenging. Numerous ML techniques have been employed to impute missing wind velocities at random building locations with minimal structural impact. This paper focuses on addressing this concern by utilizing a machine learning (ML) approach that focuses on estimating unmeasured values in critical areas near buildings. We employ three distinct ML models: the generative adversarial imputation network (GAIN), multiple imputations by chained equations (MICE), and neighbor distance imputation (NDI) to estimate missing values around building structures. Our results indicate that the GAIN technique achieves a remarkable balance, displaying the lowest average mean square error of 0.073 and the highest average R-squared error of 0.965. Furthermore, it effectively captures the distribution of measured values and provides reliable data for evaluating aerodynamic characteristics and ensuring structural safety. | ||
| Key Words | ||
| deep learning; generative adversarial imputation network; machine learning; structural safety; urban wind flow analysis; wind velocity imputation | ||
| Address | ||
| Istiak Ahammed:Department of Robot and Smart System Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu, 41566, South Korea Sujeen Song:Earth Turbine, 36 Dongdeok ro 40 gil, Jung gu, Daegu, 41905, South Korea Gang Hu:School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, China Jinwoo An:Department of Civil Engineering, College of Engineering and Computer Science, The University of Texas Rio Grande Valley, Edinburg, Texas 78539, USA Bubryur Kim:School of Space Engineering Sciences, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu, 41566, South Korea | ||