Random forest algorithm integrated with an initial basic feasible solution in buckling analysis of a two-dimensional functionally graded porous taper beam - Ravikiran Chinthalapudi; Jagadesh Kumar Jatavallabhula; Geetha Narayanan Kannaiyan; Bridjesh Pappula; Seshibe Makgato (Materials Research Express)
This study explores the buckling behavior of two-dimensional functionally graded porous taper beam, which are increasingly used in aerospace, civil, and mechanical engineering applications where structural stability, weight optimization, and material adaptability are essential. Traditional analytical models often struggle to handle the nonlinearities introduced by material gradation, porosity, and geometric tapering, especially under complex boundary conditions. To overcome these limitations, a hybrid analytical computational methodology is proposed that integrates the novel Initial Basic Feasible Solution approach with the Random Forest algorithm. The beam is modelled using hyperbolic shear deformation theory to account for transverse shear effects, while material properties vary along both the length and thickness following a power-law distribution. Porosity is included by a porosity index, and the tapering effects are captured using linear thickness and width ratios. The Initial Basic Feasible Solution method is used to define boundary conditions and provide an initial physically consistent solution, which is further enhanced by the Random Forest model to handle complex nonlinear interactions. Quantitative findings reveal that increasing the aspect ratio from 10 to 40 results in a 61.2% reduction in the critical buckling load. Conversely, increasing the taper ratio and width ratio improves the buckling load by 26.6% and 41.45%, respectively. An increase in porosity index from 0.0 to 0.3 leads to a 30.75% reduction in structural capacity and clamped-clamped boundary conditions improve stability by 21.34% over simply supported configurations. The proposed method offers a scalable, accurate, and computationally efficient tool for analyzing complex functionally graded porous taper beam, overcoming the limitations of classical beam theories and numerical solvers. This work provides practical insights for the design and optimization of advanced graded structures where conventional models fall short, establishing a novel pathway for the integration of machine learning in structural mechanics.