Hybrid uncertain buckling analysis for engineering structures through machine learning method

Abstract

Buckling analysis is fundamental yet essential for engineering structures in stability assessment, design optimization, structural design, and the like. Uncertainty as an inherent feature in engineering significantly affects structural buckling behaviour. A hybrid uncertain buckling analysis accounting aleatoric and epistemic uncertainties simultaneously is introduced. To determine the statistical features of the relevant bounds on the concerned structural response, an optimization strategy is developed based on a surrogate model. For surrogate model construction, an improved machine-learning technique is developed by possessing the global trend regression and local information fitting. A convex optimization program can be formulated to capture the global trend with optimal solutions, and subsequently, local information fitting improves the accuracy in handling unbiased datasets. The sampling method on random variables led the hybrid uncertain buckling analysis to be solved as a series of interval analyses on the established surrogate model. Essential statistical data regarding the limits of critical structural reactions can be assessed both effectively and efficiently. Additional features, such as information updates, further underscores the viability of the proposed scheme. Finally, the crucial buckling loads of two engineering structures facing hybrid uncertainty are thoroughly examined to highlight the potential benefits of the introduced method.