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RISS 인기검색어
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- 저자 : Byeonggug Kang (검색결과 3 건)
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Jongyeong Kim,Byeonggug Kang,Yongju Kwon,Seungbi Lee,Soonchul Kwon 한국해양공학회 2021 韓國海洋工學會誌 Vol.35 No.6
Overcrowding of high-rise buildings in urban zones change the airflow pattern in the surrounding areas. This causes building wind, which adversely affects the wind environment. Building wind can generate more serious social damage under extreme weather conditions such as typhoons. In this study, to analyze the wind speed and wind speed ratio quantitatively, we installed five anemometers in Haeundae, where high-rise buildings are dense, and conducted on-site monitoring in the event of typhoon OMAIS to determine the characteristics of wind over skyscraper towers surround the other buildings. At point M-2, where the strongest wind speed was measured, the maximum average wind speed in 1 min was observed to be 28.99 m/s, which was 1.7 times stronger than that at the ocean observatory, of 17.0 m/s, at the same time. Furthermore, when the wind speed at the ocean observatory was 8.2 m/s, a strong wind speed of 24 m/s was blowing at point M-2, and the wind speed ratio compared to that at the ocean observatory was 2.92. It is judged that winds 2–3 times stronger than those at the surrounding areas can be induced under certain conditions due to the building wind effect. To verify the degree of wind speed, we introduced the Beaufort wind scale. The Beaufort numbers of wind speed data for the ocean observatory were mostly distributed from 2 to 6, and the maximum value was 8; however, for the observation point, values from 9 to 11 were observed. Through this study, it was possible to determine the characteristics of the wind environment in the area around high-rise buildings due to the building wind effect.
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Taeyoon Kim,Woo-Dong Lee,Yongju Kwon,Jongyeong Kim,Byeonggug Kang,Soonchul Kwon 한국해양공학회 2022 韓國海洋工學會誌 Vol.36 No.5
Recently around the world, coastal erosion is paying attention as a social issue. Various constructions using low-crested and submerged structures are being performed to deal with the problems. In addition, a prediction study was researched using machine learning techniques to determine the wave attenuation characteristics of low crested structure to develop prediction matrix for wave attenuation coefficient prediction matrix consisting of weights and biases for ease access of engineers. In this study, a deep neural network model was constructed to predict the wave height transmission rate of low crested structures using Tensor flow, an open source platform. The neural network model shows a reliable prediction performance and is expected to be applied to a wide range of practical application in the field of coastal engineering. As a result of predicting the wave height transmission coefficient of the low crested structure depends on various input variable combinations, the combination of 5 condition showed relatively high accuracy with a small number of input variables defined as 0.961. In terms of the time cost of the model, it is considered that the method using the combination 5 conditions can be a good alternative. As a result of predicting the wave transmission rate of the trained deep neural network model, MSE was 1.3×10<SUP>-3</SUP>, I was 0.995, SI was 0.078, and I was 0.979, which have very good prediction accuracy. It is judged that the proposed model can be used as a design tool by engineers and scientists to predict the wave transmission coefficient behind the low crested structure.
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