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• W4296616189 endingPage "102295" @default.
• W4296616189 startingPage "102295" @default.
• W4296616189 abstract "Maintaining the integrity of netting is crucial for the protection of net-enclosure aquaculture facilities (NEAFs) against disastrous weather. To assess the expected structural performance of NEAF netting, a rapid prediction model based on a backpropagation neural network (BPNN) optimized by a genetic algorithm (GA) was developed. This GA-BPNN model is a preferable alternative to time-consuming traditional methods such as numerical simulations and laboratory experiments. The maximum loads on the rope and twine of the NEAF were predicted using the proposed GA-BPNN model with ocean conditions and structural properties (including current velocity, wave height, pile spacing, and rope/twine diameters) as input parameters. The prediction error (3.81%) and number of training-convergence steps (18) of the GA-BPNN model were lower than those of the BPNN model (9.28% and 33), respectively. The netting structural performance of NEAF under various conditions was also assessed by the GA-BPNN model. The results showed that the model predicted NEAF hydrodynamic response quickly and accurately." @default.
• W4296616189 created "2022-09-23" @default.
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• W4296616189 date "2022-11-01" @default.
• W4296616189 modified "2023-10-14" @default.
• W4296616189 title "Neural network model for assessing structural performance of netting in net-enclosure aquaculture facilities" @default.
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• W4296616189 doi "https://doi.org/10.1016/j.aquaeng.2022.102295" @default.
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