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http://dx.doi.org/10.7837/kosomes.2022.28.1.175

Peak Impact Force of Ship Bridge Collision Based on Neural Network Model  

Wang, Jian (Department of Naval Architecture and Ocean Engineering, Kunsan National University)
Noh, Jackyou (Department of Naval Architecture and Ocean Engineering, Kunsan National University)
Publication Information
Journal of the Korean Society of Marine Environment & Safety / v.28, no.1, 2022 , pp. 175-183 More about this Journal
Abstract
The collision between a ship and bridge across a waterway may result in extremely serious consequences that may endanger the safety of life and property. Therefore, factors affecting ship bridge collision must be investigated, and the impact force should be discussed based on various collision conditions. In this study, a finite element model of ship bridge collision is established, and the peak impact force of a ship bridge collision based on 50 operating conditions combined with three input parameters, i.e., ship loading condition, ship speed, and ship bridge collision angle, is calculated via numerical simulation. Using neural network models trained with the numerical simulation results, the prediction model of the peak impact force of ship bridge collision involving an extremely short calculation time on the order of milliseconds is established. The neural network models used in this study are the basic backpropagation neural network model and Elman neural network model, which can manage temporal information. The accuracy of the neural network models is verified using 10 test samples based on the operating conditions. Results of a verification test show that the Elman neural network model performs better than the backpropagation neural network model, with a mean relative error of 4.566% and relative errors of less than 5% in 8 among 10 test cases. The trained neural network can yield a reliable ship bridge collision force instantaneously only when the required parameters are specified and a nonlinear finite element solution process is not required. The proposed model can be used to predict whether a catastrophic collision will occur during ship navigation, and thus hence the safety of crew operating the ship.
Keywords
Finite element method; Artificial neural network prediction; Ship bridge collision; Peak impact force; Backpropagation neural network; Elman neural network;
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