Lower-bound axial buckling load prediction for isotropic cylindrical shells using probabilistic random perturbation load approach

Due to high sensitivity to various imperfections, buckling loads of thin-walled cylindrical shells subjected to axial load vary dramatically. In order to predict lower-bound buckling loads for axially loaded cylindrical shells rationally, a probabilistic analysis approach named Probabilistic Random Perturbation Load Approach (PRPLA) is developed in this study. Firstly, a Back-Propagation Neural Network (BPNN) based method is established to describe measured imperfection patterns. Next, Random Single Perturbation Load Approach (RSPLA) is loaded upon BPNN-based depicted traditional imperfection patterns to construct a stochastic dimple imperfection. The aforementioned scattering traditional imperfections, as well as a variety of scattering non-traditional imperfections, are then sampled using Monte-Carlo simulation to generate cylindrical shell models differentiating from a nominal one. The probabilistic distribution of lower-bound buckling loads is obtained by finite element analysis. A nominal shell's realistic lower-bound buckling load is determined by choosing a specified reliability level lastly. The results show that describing measured imperfection patterns via BPNN is very close to real ones, and PRPLA presented is an improved method to find lower-bound buckling loads efficiently compared with NASA SP-8007 and many commonly used numerical approaches.