• Structural Engineering and Mechanics
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• v.25 no.1
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• pp.127-130
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• 2007

• Structural Engineering and Mechanics
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• v.29 no.2
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• pp.237-240
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• 2008

• Steel and Composite Structures
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• v.29 no.6
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• pp.759-770
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• 2018

In the present investigation, by using the two numerical methods, free vibration analysis of laminated annular and annular sector plates have been studied. In order to obtain the main equations two different shell theories such as Love's shell theory and first-order shear deformation theory (FSDT) have been used for modeling. After obtaining the fundamental equations in briefly, the methods of harmonic differential quadrature (HDQ) and discrete singular convolution (DSC) are used to solve the equation of motion. Accuracy, convergence and reliability of the present HDQ and DSC methods were tested by comparing the existing results obtained by different methods in the literature. The effects of some geometric and material properties of the plates are investigated via these two methods. The advantages and accuracy of the HDQ and DSC methods have also been examined with different grid numbers and shell theory. Some results for laminated annular plates and laminated circular plates were also been supplied.

• Structural Engineering and Mechanics
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• v.83 no.2
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• pp.259-272
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• 2022

The branch of electronics that uses an organic solar cell or conductive organic polymers in order to yield electricity from sunlight is called photovoltaic. Regarding this crucial issue, an artificial intelligence-based predictor is presented to investigate the vibrational behavior of the organic solar cell. In addition, the generalized differential quadrature method (GDQM) is utilized to extract the results. The validation examination is done to confirm the credibility of the results. Then, the deep neural network with fully connected layers (DNN-FCL) is trained by means of Adam optimization on the dataset whose members are the vibration response of the design-points. By determining the optimum values for the biases along with weights of DNN-FCL, one can predict the vibrational characteristics of any organic solar cell by knowing the properties defined as the inputs of the mentioned DNN. To assess the ability of the proposed artificial intelligence-based model in prediction of the vibrational response of the organic solar cell, the authors monitored the mean squared error in different steps of the training the DNN-FCL and they observed that the convergency of the results is excellent.