Prediction of transmission coefficient of double-row perforated cylinder breakwater based on SSA-CNN model

Deng Bin; Wang Ling; He Jun; Yin Longbin; Jiang Changbo; Chen Jie; Wu Zhiyuan

doi:10.12284/hyxb2024035

Volume 46 Issue 4

Jun. 2024

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Article Navigation > Haiyang Xuebao > 2024 > 46(4): 122-132

Deng Bin，Wang Ling，He Jun, et al. Prediction of transmission coefficient of double-row perforated cylinder breakwater based on SSA-CNN model[J]. Haiyang Xuebao，2024, 46(4)：122–132 doi: 10.12284/hyxb2024035

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Prediction of transmission coefficient of double-row perforated cylinder breakwater based on SSA-CNN model

doi: 10.12284/hyxb2024035

Deng Bin^{1, 2, 4
,},
Wang Ling¹,
He Jun^{3
,
,},
Yin Longbin¹,
Jiang Changbo^{1, 2},
Chen Jie^{1, 2},
Wu Zhiyuan^{1, 2}

1.
School of Hydraulic and Environmental Engineering, Changsha University of Science & Technology, Changsha 410114, China
2.
Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province, Changsha 410114, China
3.
CCCC Water Transportation Consultants Co., Ltd., Beijing 100007, China
4.
State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation, Tianjin 300072, China

Received Date: 2024-01-11
Rev Recd Date: 2024-03-21

Available Online: 2024-05-20

Publish Date: 2024-06-30

Abstract

Abstract

The double-row perforated cylinder breakwater is a new type of environment-friendly breakwater, and the research on its wave absorbing characteristics is of great engineering significance. With the development of artificial intelligence, solving the water dynamics problem of breakwater based on machine learning technology has become a new research paradigm. This paper proposes a Convolutional Neural Network (CNN) model based on Sparrow Search Algorithm (SSA) to achieve intelligent optimization prediction of transmission coefficient of double-row perforated cylindrical breakwater. The results show that: (1) wave height, wave period, wavelength, wave velocity, row spacing, hole rate and water depth are identified as the key factors affecting the transmission coefficient. (2) When the population size of the SSA-CNN model is 10, the R² value of the wave transmission coefficient prediction reaches 0.9909, and the average relative error is reduced by 22.24% compared with the single CNN model. The research results provide a new optimal prediction model for the study of wave transmission by using neural networks.
- double-row perforated cylinder breakwater,
- wave absorbing characteristic,
- sparrow search algorithm,
- convolutional neural network,
- transmission coefficient

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References(32)

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