Abstract
The paper is focused on uncertainty quantification of soil-structure interaction in tunnel linings using a surrogate model in form of Polynomial Chaos Expansion (PCE). Tunnel design is a complex and complicated task since it is strongly associated with a great number of load and material uncertainties. Moreover, modelling the soil-structure interaction multiplies the complexity and non-linearity of a tunnel engineering problems. In order to handle such uncertainties, finite element method with random input variables has proven to be a very accurate tool. The probabilistic analysis is typically performed by Monte Carlo simulation (MC), simulating uncertainties according to their complete probability distributions and statistical correlations. The computational burden of MC represents the main obstacle to its use in complex numerical models and it is therefore not practical for industrial applications. The solution can be an efficient approximation of the original mathematical model by computationally efficient analytical function – a surrogate model. In this study, the surrogate model in form of PCE is utilized, allowing for analytical post-processing (statistical and sensitivity analysis). Uncertainty quantification is focused on estimation of spatial variability of internal forces caused by the soil-structure interaction.
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Acknowledgments
Theoretical background of the study was supported by Czech Science Foundation under project No. 24-10892S, the practical implementation of the numerical algorithm was part of the project DELTA supported by Technological Agency of Czech Republic under project No. TM04000012. The international collaboration of authors was supported by bilateral Czech-Germany mobility project No. 8J24DE002 supported by Ministry of Education, Youth and Sports of Czech Republic and DAAD.
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Novák, L., Gakis, A., Křížek, M., Novák, D., Spyridis, P. (2024). Uncertainty Quantification of Soil-Structure Interaction in Tunnel Linings by Polynomial Chaos Expansion. In: Matos, J.C., et al. 20th International Probabilistic Workshop. IPW 2024. Lecture Notes in Civil Engineering, vol 494. Springer, Cham. https://doi.org/10.1007/978-3-031-60271-9_48
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DOI: https://doi.org/10.1007/978-3-031-60271-9_48
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