Computationally aware estimation of ultimate strength reduction of stiffened panels caused by welding residual stress

From finite element to data-driven methods

Journal Article (2022)
Authors

Shen Li (University of Strathclyde)

A. Coraddu (TU Delft - Ship Design, Production and Operations)

L. Oneto (University of Genova)

Research Group
Ship Design, Production and Operations
Copyright
© 2022 Shen Li, A. Coraddu, Luca Oneto
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 Shen Li, A. Coraddu, Luca Oneto
Research Group
Ship Design, Production and Operations
Volume number
264
DOI:
https://doi.org/10.1016/j.engstruct.2022.114423
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Abstract

Ultimate limit state (ULS) assessment examines the maximum load-carrying capacity of structures considering inelastic buckling failure. Contrary to the traditional allowable stress principle which is mainly based on experiences, the ULS assessment focuses on explicitly evaluating the structural safety margin and thus enables a consistent level of safety/risk between conventional and novel structural designs. Modern structures are usually designed as a network of plates and stiffeners (e.g., ship structures, offshore and onshore wind turbine, and land-based bridge) joined by welding which induces a residual stress field. Hence, predicting the ultimate strength reduction of stiffened panels caused by welding residual stress is a crucial problem addressed by many scholars with different approaches, among which the Nonlinear Finite Element Method (NLFEM) is the prevailing approach within the community of structural engineering. Unfortunately, the NLFEM has a high computational requirement which prevents its use in the design, appraisal, and optimisation phases of stiffened panels. To well approximate the nonlinear finite element method, a data-driven method is proposed in this paper, with a functional which is computationally expensive to build but computationally inexpensive to use allowing its application at design stage. Results obtained in different (i.e., interpolation and extrapolation) scenarios using data generated by a state-of-the-art NLFEM on a series of stiffened panels will support the proposed method.