In this paper an analytical procedure is given to study the free vibration characteristics of laminated non-homogeneous orthotropic thin circular cylindrical shells resting on elastic foundation, accounting for Karman type geometric non-linearity. At first, the basic relations and modified Donnell type stability equations, considering finite deformations, have been obtained for laminated thin orthotropic circular cylindrical shells, the Young's moduli of which varies piecewise continuously in the thickness direction. Applying Galerkin method to the latter equations, a non-linear time dependent differential equation is obtained for the displacement amplitude. The frequency is obtained from this equation as a function of the shell displacement amplitude. Finally, the effect of elastic foundation, non-linearity, non-homogeneity, the number and ordering of layers on the frequency is found for different mode numbers. These results are given in the form of tables and figures. The present analysis is validated by comparing results with those in the literature.
The shear strength (S u) of soils is one of the most widely used parameters for designing structures safely, where Su is found with the unconfined compression test (UCS). Although UCS can be acquired by performing uniaxial compression test it would be extremely helpful to predict the UCS without performing any compression test, namely, using computational methods considering different parameters of soils such as consistency limits, fine grain ratio, liquidity index, and void ratio. The goal of present work is predicting UCS taking into account these soil parameters with the aid of developed Adaptive Neuro-Fuzzy Inference System (ANFIS) model and the Multiple Linear Regression (MLR) analyses. On the other hand, the effect of the size of the training set of designed models on the results is examined, also. For this aim, four different models composed of different training and test set ratios have been constructed and analyzed using ANFIS and MLR. It is concluded that UCS can be predicted using MLR analysis and ANFIS model with best 0.76 and 0.91 values of determination coefficient (R 2) around the x = y line respectively, and the effect of the size of the training set of models on ANFIS is more pronounced than MLR models.
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