In many engineering problems, the response function such as the strain or stress field of the structure, its load-bearing capacity, deflection, etc., comes from a finite element method discretization and is therefore very expensive to evaluate. For this reason, methods that replace the original computationally expensive (high-fidelity) model with a simpler (low-fidelity) model that is fast to evaluate are desirable. This paper is focused on the comparison of two surrogate modeling techniques and their potential for stochastic analysis of engineering structures; polynomial chaos expansion and artificial neural network are compared in two typical engineering applications. The first example represents a typical engineering problem with a known analytical solution, the maximum deflection of a fixed beam loaded with a single force. The second example represents a real-world implicitly defined and computationally demanding engineering problem, an existing bridge made of post-tensioned concrete girders.
CITATION STYLE
Lehký, D., Novák, L., & Novák, D. (2023). Surrogate Modeling for Stochastic Assessment of Engineering Structures. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13811 LNCS, pp. 388–401). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-25891-6_29
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