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Lookup NU author(s): Dr Aleksandra Svalova, Dr Peter Helm, Dr Dennis Prangle, Dr Mohamed Rouainia, Professor Stephanie Glendinning, Professor Darren Wilkinson
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
We propose using fully Bayesian Gaussian process emulation (GPE) as a surrogate for expensive computer experiments of transport infrastructure cut slopes in high-plasticity clay soils that are associated with an increased risk of failure. Our deterioration experiments simulate the dissipation of excess pore water pressure and seasonal pore water pressure cycles to determine slope failure time. It is impractical to perform the number of computer simulations that would be sufficient to make slope stability predictions over a meaningful range of geometries and strength parameters. Therefore, a GPE is used as an interpolator over a set of optimally spaced simulator runs modeling the time to slope failure as a function of geometry, strength, and permeability. Bayesian inference and Markov chain Monte Carlo simulation are used to obtain posterior estimates of the GPE parameters. For the experiments that do not reach failure within model time of 184 years, the time to failure is stochastically imputed by the Bayesian model. The trained GPE has the potential to inform infrastructure slope design, management, and maintenance. The reduction in computational cost compared with the original simulator makes it a highly attractive tool which can be applied to the different spatio-temporal scales of transport networks.
Author(s): Svalova A, Helm P, Prangle D, Rouainia M, Glendinning S, Wilkinson DJ
Publication type: Article
Publication status: Published
Journal: Data-Centric Engineering
Year: 2021
Volume: 2
Online publication date: 06/09/2021
Acceptance date: 06/07/2021
Date deposited: 07/09/2021
ISSN (electronic): 2632-6736
Publisher: Cambridge University Press
URL: https://doi.org/10.1017/dce.2021.14
DOI: 10.1017/dce.2021.14
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