Uncertainty qualification in seismic analysis of concrete dams based on model order reduction accelerated stochastic SBFEM
A pioneering framework for uncertainty quantification in seismic analysis of concrete dams rooted in the stochastic scaled boundary finite element method (SSBFEM) is proposed. For seismic analysis of dams, octree decomposition is used for mesh generation. SSBFEM is developed to predict the structura...
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Veröffentlicht in: | Soil dynamics and earthquake engineering (1984) 2024-06, Vol.181, p.108683, Article 108683 |
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Sprache: | eng |
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Zusammenfassung: | A pioneering framework for uncertainty quantification in seismic analysis of concrete dams rooted in the stochastic scaled boundary finite element method (SSBFEM) is proposed. For seismic analysis of dams, octree decomposition is used for mesh generation. SSBFEM is developed to predict the structural responses with randomly distributed material properties. The script in this paper can integrate octree mesh and SSBFEM into ABAQUS user element (UEL). Implementing octree SSBFEM on commercial software provides accurate and convenient full order models for uncertainty quantification. Monte Carlo simulation (MCs) is deployed to calculate the statistical characteristics of structural responses under random variables. Additionally, singular value decomposition (SVD) and radial basis function (RBF) are leveraged to refine traditional MCs. The solution space of MCs is decomposed into lower-order subspaces. Any structural responses can be rapidly evaluated from linear combinations of subspaces. Particularly, the method has been successfully applied to seismic analysis of concrete dams with different random material properties. Finally, several illustrative examples are provided to validate the accuracy and efficacy of the algorithm.
•A Sample-based stochastic scaled boundary finite element method (SSBFEM) is integrated into commercial software.•A novel Monte Carlo simulation (MCs) framework for seismic analysis of concrete dams is introduced.•Singular value decomposition (SVD) and radial basis function (RBF) are used to accelerate uncertainty qualification. |
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ISSN: | 0267-7261 1879-341X |
DOI: | 10.1016/j.soildyn.2024.108683 |