Buy article PDF
The purchased file will be sent to you
via email after the payment is completed.
US$ 35
|
Wind and Structures Volume 36, Number 4, April 2023 , pages 237-247 DOI: https://doi.org/10.12989/was.2023.36.4.237 |
|
|
|
Enhancement of durability of tall buildings by using deep-learning-based predictions of wind-induced pressure |
||
K.R. Sri Preethaa, N. Yuvaraj, Gitanjali Wadhwa, Song Sujeen, Se-Woon Choi and Bubryur Kim
|
||
| Abstract | ||
| The emergence of high-rise buildings has necessitated frequent structural health monitoring and maintenance for safety reasons. Wind causes damage and structural changes on tall structures; thus, safe structures should be designed. The pressure developed on tall buildings has been utilized in previous research studies to assess the impacts of wind on structures. The wind tunnel test is a primary research method commonly used to quantify the aerodynamic characteristics of high-rise buildings. Wind pressure is measured by placing pressure sensor taps at different locations on tall buildings, and the collected data are used for analysis. However, sensors may malfunction and produce erroneous data; these data losses make it difficult to analyze aerodynamic properties. Therefore, it is essential to generate missing data relative to the original data obtained from neighboring pressure sensor taps at various intervals. This study proposes a deep learning-based, deep convolutional generative adversarial network (DCGAN) to restore missing data associated with faulty pressure sensors installed on high-rise buildings. The performance of the proposed DCGAN is validated by using a standard imputation model known as the generative adversarial imputation network (GAIN). The average mean-square error (AMSE) and average R-squared (ARSE) are used as performance metrics. The calculated ARSE values by DCGAN on the building model's front, backside, left, and right sides are 0.970, 0.972, 0.984 and 0.978, respectively. The AMSE produced by DCGAN on four sides of the building model is 0.008, 0.010, 0.015 and 0.014. The average standard deviation of the actual measures of the pressure sensors on four sides of the model were 0.1738, 0.1758, 0.2234 and 0.2278. The average standard deviation of the pressure values generated by the proposed DCGAN imputation model was closer to that of the measured actual with values of 0.1736,0.1746,0.2191, and 0.2239 on four sides, respectively. In comparison, the standard deviation of the values predicted by GAIN are 0.1726,0.1735,0.2161, and 0.2209, which is far from actual values. The results demonstrate that DCGAN model fits better for data imputation than the GAIN model with improved accuracy and fewer error rates. Additionally, the DCGAN is utilized to estimate the wind pressure in regions of buildings where no pressure sensor taps are available; the model yielded greater prediction accuracy than GAIN. | ||
| Key Words | ||
| deep convolutional generative adversarial network; deep learning; generative adversarial imputation model; highrise buildings; wind-tunnel test | ||
| Address | ||
| K.R. Sri Preethaa and N. Yuvaraj:1)Department of Robot and Smart System Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, 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 Sujeen Song:Earth Turbine, 36 Dongdeok-ro 40-gil, Jung-gu, Daegu 41905, Korea Se-Woon Choi:Department of Architectural Engineering, Daegu Catholic University, Hayang-Ro 13-13, Hayang-Eup, Gyeongsan-si, Gyeonqbuk, 38430, Korea Bubryur Kim:Department of Robot and Smart System Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Korea | ||