Finite element analysis of functionally graded sandwich plates with porosity via a new hyperbolic shear deformation theory

This study focusses on establishing the finite element model based on a new hyperbolic sheareformation theory to investigate the static bending,free vibration,and buckling of the functionally graded sandwich plates with porosity.The novel sandwich plate consists of one homogenous ceramic core and two different functionally graded face sheets which can be widely applied in many fields of engineering and defence technology.The discrete governing equations of motion are carried out via Hamilton’s principle and finite element method.The computation program is coded in MATLAB software and used to study the mechanical behavior of the functionally graded sandwich plate with porosity.The present finite element algorithm can be employed to study the plates with arbitrary shape and boundary conditions.The obtained results are compared with available results in the literature to confirm the reliability of the present algorithm.Also,a comprehensive investigation of the effects of several parameters on the bending,free vibration,and buckling response of functionally graded sandwich plates is presented.The numerical results shows that the distribution of porosity plays significant role on the mechanical behavior of the functionally graded sandwich plates。

A hierarchical system to predict behavior of soil and cantilever sheet wall by data-driven models

The study proposes a framework combining machine learning(ML)models into a logical hierarchical system which evaluates the stability of the sheet wall before other predictions.The study uses the hardening soil(HS)model to develop a 200-sample finite element analysis(FEA)database,to develop the ML models.Consequently,a system containing three trained ML models is proposed to first predict the stability status(random forest classification,RFC)followed by 1)the cantilever top horizontal displacement of sheet wall(artificial neural network regression models,RANN1)and 2)vertical settlement of soil(RANN2).The uncertainty of this data-driven system is partially investigated by developing 1000 RFC models,based on the application of random sampling technique in the data splitting process.Investigation on the distribution of the evaluation metrics reveals negative skewed data toward the 1.0000 value.This implies a high performance of RFC on the database with medians of accuracy,precision,and recall,on test set are 1.0000,1.0000,and 0.92857,respectively.The regression ANN models have coefficient of determinations on test set,as high as 0.9521 for RANN1,and 0.9988 for RANN2,respectively.The parametric study for these regressions is also provided to evaluate the relative insight influence of inputs to output.