Paper
9 December 2021 Analysis damage mechanism of Shuangjiantu seawall based on marine hydrographic data monitored during the typhoon of “In-fa”
Bei Xu, Zhou Ye, Wenwei Gu
Author Affiliations +
Proceedings Volume 12129, International Conference on Environmental Remote Sensing and Big Data (ERSBD 2021); 121290J (2021) https://doi.org/10.1117/12.2625549
Event: 2021 International Conference on Environmental Remote Sensing and Big Data, 2021, Wuhan, China
Abstract
In order to comprehensively understand the main reasons for the damage of Shuangjiantu seawall under the influence of strong typhoon, based on the analysis of a large amount of marine hydrographic data monitored during the active period of Typhoon No.6 “In-fa” in 2021, the marine environment around Shuangjiantu seawall in Zhoushan was projected and the basic situation of Shuangjiantu seawall design and construction was analyzed to find by analyzing the basic design and construction of Shuangjiantu seawall, we found out the damage characteristics and mechanism of typhoon “In-fa” on Shuangjiantu seawall in Zhoushan. It was found that the seawall was most affected when the seawall orientation overlapped with the strong wind direction. The reason for the damage of Shuangjiantu seawall: the typhoon hit the seawall head-on after landing, and the infinite wind area in front of the seawall constituted a bad marine environment condition in front of the seawall. Although the overall design of the seawall met the standard, but did not consider the impact of local water depth topographic differences on the seawall, different sections at the same structure and strength. The water depth at the larger wave is not easy to break, there is a greater force on the seawall, so the seawall damage mainly appeared in the dike in front of the local water depth deeper location.
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Bei Xu, Zhou Ye, and Wenwei Gu "Analysis damage mechanism of Shuangjiantu seawall based on marine hydrographic data monitored during the typhoon of “In-fa”", Proc. SPIE 12129, International Conference on Environmental Remote Sensing and Big Data (ERSBD 2021), 121290J (9 December 2021); https://doi.org/10.1117/12.2625549
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KEYWORDS
Oceanography

Water

Statistical analysis

Wavefronts

Environmental monitoring

Eye

Floods

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