Neural network-based DPIM for uncertainty quantification of imperfect cylindrical stiffened shells with multiple random parameters-Reference-Cited by-同舟云学术

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Neural network-based DPIM for uncertainty quantification of imperfect cylindrical stiffened shells with multiple random parameters

Published:2024-09 Issue: Volume:166 Page:105795
ISSN:0955-7997
Container-title:Engineering Analysis with Boundary Elements
language:en
Short-container-title:Engineering Analysis with Boundary Elements

Author:

Chen Hanshu,Chen Guohai,Yang Dixiong,Fu Zhuojia^ORCID

Publisher

Elsevier BV

Reference50 articles.

1. The effect of material and thickness variability on the buckling load of shells with random initial imperfections;Papadopoulos;Comput Methods Appl Mech Eng,2005

2. Optimal design of trapezoid stiffeners of composite cylindrical shells subjected to hydrostatic pressure;Wei;Thin Wall Struct,2021

3. Non-intrusive stochastic analysis with parameterized imprecise probability models: II. Reliability and rare events analysis;Wei;Mech Syst Signal Process,2019

4. Novel bootstrap-based ellipsoidal convex model for non-probabilistic reliability-based design optimization with insufficient input data;Yang;Comput Methods Appl Mech Eng,2023

5. Uncertain buckling: its past, present and future;Elishakoff;Int J Solids Struct,2000

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