Displacements, velocities and accelerations of Six Degree of freedom of a single floating structure was predicted using Time Series NARX feedback neural Networks. The nonlinear autoregressive network with exogenous inputs (NARX) is a recurrent dynamic network, with feedback connections enclosing several layers of the network is based on the linear ARX model, which is commonly used in time-series modelling is used in this study. Time series data of displacements of a single floating structure was used for training and testing the ANN model. In the training stage, this time series data of environment parameters was used as input and dynamic responses was used as target. Benchmarking result and error prediction was compared between two techniques of Neural Network training. The prediction result of the model responses can be concluded that NARX with mirroring technique increase the accuracy and can be used to predict time series of dynamic responses of floating structures.
This article presents the natural frequency analysis of beam framework and axial force effect. The analysis considers the modification of mass matrix, rigidity, and geometric matrix of semi-rigid beam framework. The rigidity matrix, mass, and semi-rigid geometric connecting rod were derived from polynomial Hermite modification. This paper shows an example of natural frequency of beam framework with axial force effect or not. The results of analysis were used to show the efficiency of the calculation of beam frame work model with semi-rigid connection, which results in a more realistic solution than rigid connection model. This paper also shows that the change of axial force can result in the change of natural frequency of beam framework. Besides, this method can be used to examine any building because it can calculate the effect of structural rigidity
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