Surrogate Modeling for Stochastic Assessment of Engineering Structures

Surrogate Modeling for Stochastic Assessment of Engineering Structures

Paper in proceedings (conference paper)

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.

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.

Artificial neural networks; Polynomial chaos expansion; Stochastic assessment; Surrogate modelling; Uncertainties propagation

Artificial neural networks; Polynomial chaos expansion; Stochastic assessment; Surrogate modelling; Uncertainties propagation

LEHKÝ, D.; NOVÁK, L.; NOVÁK, D.

2024

09.03.2023

Springer Science and Business Media Deutschland GmbH

Germany

9783031258909

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

388

401

14