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- W4200617512 abstract "This paper presents a machine learning method to predict the dynamic and structural behaviors of the submerged floating tunnel (SFT) based on an artificial neural network (ANN) and numerical sensors. The training and testing data are generated by the tunnel-mooring coupled time-domain hydro-elastic simulations under various random wave excitations. Then, ANN is constructed with an input layer, hidden layers, and an output layer. The input layer consists of the numerical angle and acceleration signals while the output is the tunnel's estimated displacements, bending moments, and mooring tensions. The number of hidden layers, neurons in each layer, and epochs for stable performance are selected based on the parametric study. For high prediction accuracy, Rectified Linear Unit (ReLU) and Root Mean Square Propagation (RMSProp) are adopted as activation functions and optimizers. In addition, a cost-effective sensor combination at an optimal location is investigated to achieve good performance while using a minimal number of sensors. The optimized sensor combination with one angle sensor and one accelerometer successfully predicts the dynamic and structural responses based on not only R-squared and root-mean-square errors but also representative time histories, spectra, and prediction accuracy plots." @default.
- W4200617512 created "2021-12-31" @default.
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- W4200617512 date "2022-01-01" @default.
- W4200617512 modified "2023-09-23" @default.
- W4200617512 title "Prediction of dynamic and structural responses of submerged floating tunnel using artificial neural network and minimum sensors" @default.
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- W4200617512 doi "https://doi.org/10.1016/j.oceaneng.2021.110402" @default.
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