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Neural network prediction of buckling load of steel arch-shells
Calculating the buckling load of structures is one of the main aspects of geometric structural instability. With reference to finite element software (ANSYS) and considering the weight of the structure as a constant load, the paper calculates the buckling load of steal arch shells as the main scope. Then, the article makes a prediction for the buckling load of steel arch shells applying 85 datasets prepared by the above mentioned method and using an artificial neural network by means of NeuroSolution 5.0 software. The radius of the periphery cylinder of an arch shell, the thickness of the shell and its internal angel are considered as inputs of construct models. Next, in order to attain an optimum model calculating and comparing the value of Root Mean Squared Error, RMSE, Mean Absolute Error, MAE and the coefficient of correlation, R2, for all 1 and 2-layer constructed models, a model having architecture 3-7-1 was found to be optimum. A high confirmed correlation with calculated buckling loads employing the finite element method and considering the calculated values of RMSE (0.0126), MAE (0.011) and the obtained value R2 (0.998) demonstrated high efficiency of a new developed neural network model.
Neural network prediction of buckling load of steel arch-shells
Calculating the buckling load of structures is one of the main aspects of geometric structural instability. With reference to finite element software (ANSYS) and considering the weight of the structure as a constant load, the paper calculates the buckling load of steal arch shells as the main scope. Then, the article makes a prediction for the buckling load of steel arch shells applying 85 datasets prepared by the above mentioned method and using an artificial neural network by means of NeuroSolution 5.0 software. The radius of the periphery cylinder of an arch shell, the thickness of the shell and its internal angel are considered as inputs of construct models. Next, in order to attain an optimum model calculating and comparing the value of Root Mean Squared Error, RMSE, Mean Absolute Error, MAE and the coefficient of correlation, R2, for all 1 and 2-layer constructed models, a model having architecture 3-7-1 was found to be optimum. A high confirmed correlation with calculated buckling loads employing the finite element method and considering the calculated values of RMSE (0.0126), MAE (0.011) and the obtained value R2 (0.998) demonstrated high efficiency of a new developed neural network model.
Neural network prediction of buckling load of steel arch-shells
Archiv.Civ.Mech.Eng
Hasanzadehshooiili, H. (Autor:in) / Lakirouhani, A. (Autor:in) / Šapalas, A. (Autor:in)
Archives of Civil and Mechanical Engineering ; 12 ; 477-484
01.12.2012
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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