인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
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지원사업
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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초록·키워드
This paper introduces a highly accurate and computationally efficient shear deformable finite element model for the static analysis of bi-directional functionally graded beams (BD-FGBs) with various boundary conditions grounded in the first-order shear deformation theory (FSDT). The model, featuring ten degrees of freedom across five nodes, excels in capturing both axial and shear deformations with remarkable precision while maintaining a streamlined formulation. In a novel approach, Artificial Neural Network (ANN) methods are also employed alongside the finite element analysis, offering a dual-method investigation into the static behavior of BD-FGBs. This paper aims to further advance the understanding of BD-FGM beams by exploring their static behavior under diverse loading conditions and boundary constraints, employing advanced finite element methods and artificial neural network techniques. The material properties are modeled through power-law distributions, and the governing equations are derived from Lagrange’s principle. Displacements and stresses were computed under different boundary conditions (BCs), slenderness ratios (L/h), and power-law indices (px, pz). Comparative analysis with existing literature reveals the superior suitability of the proposed finite element model for static analysis, while the ANN approach further reinforces its potential as a robust, complementary tool. The innovative combination of these methods promises to offer significant contributions to the field and provides new insights into the behavior of BD-FGBs under static loads.
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