TY  - EJOU
AU  - Liang, Yan-Ping 
AU  - Wan, Zhiqiang 

TI  - Interpretable Machine-Learning-Assisted Stochastic Buckling Assessment of Cylindrical Shells with Random Geometric Imperfections
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2026
VL  - 148
IS  - 3
SN  - 1526-1506

AB  - Initial geometric imperfections critically affect cylindrical-shell buckling, yet the influence of imperfection morphology remains difficult to separate from that of amplitude. This study develops an interpretable machine-learning-assisted framework for stochastic buckling assessment of cylindrical shells with random geometric imperfections. Circumferentially continuous Gaussian imperfection fields with different normalized correlation lengths are generated under a fixed root-mean-square (RMS) amplitude. Nonlinear Riks analyses are performed to construct a finite-element database of the corresponding buckling responses. Feature diagnosis is used to identify response-relevant descriptors of imperfection morphology. Compact surrogate models are subsequently evaluated for sample-level prediction and group-level statistical trend representation. The buckling resistance first decreases and then increases with increasing correlation length, demonstrating a pronounced non-monotonic morphology effect. Among the considered descriptors, the RMS edge jump is identified as the most informative field-derived measure and provides robust predictive information across multiple regression models. The proposed framework provides a compact link between imperfection morphology and stochastic buckling response.
KW  - Cylindrical shell; stochastic buckling; random geometric imperfection; machine learning; feature diagnosis; surrogate modeling

DO  - 10.32604/cmes.2026.088012