RESUMO
This paper presents a semi-analytical approach for investigating the nonlinear buckling and postbuckling of spiral corrugated sandwich functionally graded (FGM) cylindrical shells under external pressure and surrounded by a two-parameter elastic foundation based on Donnell shell theory. The improved homogenization theory for the spiral corrugated FGM structure is applied and the geometrical nonlinearity in a von Karman sense is taken into account. The nonlinear equilibrium equation system can be solved by using the Galerkin method with the three-term solution form of deflection. An explicit solution form for the nonlinear buckling behavior of shells is obtained. The critical buckling pressure and the postbuckling strength of shells are numerically investigated. Additionally, the effects of spiral corrugation in enhancing the nonlinear buckling behavior of spiral corrugated sandwich FGM cylindrical shells are validated and discussed.
RESUMO
In this paper compressive strength and ultimate strain results in the current database of fiber-reinforced polymer (FRP)-confined concrete are used to determine the reliability of their design space. The Lognormal, Normal, Frechet, Gumbel, and Weibull distributions are selected to evaluate the probabilistic characteristics of six FRP material categories. Following this, safety levels of the database are determined based on a probabilistic model. An iterative reliability method is developed with conjugate search direction for evaluating the reliability. The results show that Lognormal and Gumbel distributions provide best probability distribution for model errors of strength and strain enhancement ratios. The developed conjugate reliability method provides improved robustness over the existing reliability methods owing to its faster convergence to stable results. The results reveal that the part of the database containing normal strength concrete (NSC) heavily confined (i.e., actual confinement ratio (flu,a/f'co) > 0.5) by low and normal modulus carbon fibers (i.e., fiber elastic modulus (Ef) ≤ 260 GPa) and moderately confined (i.e., 0.3 ≤ flu,a/f'co ≤ 0.5) by aramid fibers exhibits a very high safety level. The segments of the database with a low and moderate safety level have been identified as i) NSC moderately and heavily confined by higher modulus glass fibers (i.e., Ef > 60 GPa), ii) high strength concrete (HSC) moderately and heavily confined (i.e., flu,a/f'co > 0.3) by glass fibers, iii) HSC lightly confined (i.e., flu,a/f'co ≤ 0.2) by carbon fibers, and iv) HSC lightly confined by aramid fibers. Additional experimental studies are required on these segments of the database before they can be used reliably for design and modeling purposes.
RESUMO
The objective of this innovative research is assessment of dynamic stability for a hybrid nanocomposite polymer beam. The considered beam formed by multiphase nanocomposite, including polymer-carbon nanotubes (CNTs)-carbon fibers (CFs). Hence, as to compute the effective material characteristics related to multiphase nanocomposite layers, the Halpin-Tsai model, as well as micromechanics equations are employed. To model the structure realistically, exponential shear deformation beam theory (ESDBT) is applied and using energy methods, governing equations are achieved. Moreover, differential quadrature method (DQM) as well as Bolotin procedures are used for solving the obtained governing equations and the dynamic instability region (DIR) relative to the beam is determined. To extend this novel research, various parameters pinpointing the influences of CNT volume fraction, CFs volume percent, boundary edges as well as the structure's geometric variables on the dynamic behavior of the beam are presented. The results were validated with the theoretical and experimental results of other published papers. The outcomes reveal that increment of volume fraction of CNT is able to shift DIR to more amounts of frequency. Further, rise of carbon fibers volume percent leads to increase the excitation frequency of this structure.
RESUMO
In the present study, a hybrid intelligent model called SVR_RSM, which was extracted using response surface method (RSM) combined by the support vector regression (SVR) approaches was applied for predicting monthly pan evaporation (Epan). This method is established based on two basic calibrating process using RSM and SVR. In the first process, an input data group with two different input variables are used to calibrate the RSM; hence, the calibrating data by RSM in the first process are applied as input database for calibrating the SVR in the second process. Results obtained using the proposed SVR_RSM was compared with those obtained using the RSM, SVR, and the well-known multilayer perceptron neural network (MLPNN) models. Climatic variables including maximum and minimum temperatures (Tmax, Tmin), wind speed (U2), and relative humidity (H%), and the periodicity represented by the month number (α) were selected for predicting the monthly Epan measured with the standard class A evaporation pan. Data was collected at six climatic stations located at the northern East of Algeria. The performances of the proposed models were compared using the RMSE, MAE, modified index of agreement (d), coefficient of correlation (R), and modified Nash and Sutcliffe efficiency (NSE). Using various input combination, the results show that the hybrid SVR_RSM model performed better than all the proposed models. Overall, better accuracy was observed when the model contained the periodicity (α), and it was demonstrated that the best accuracy was obtained using only Tmax and Tmin, coupled with the periodicity.
