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Emulating computer experiments of transport infrastructure slope stability using Gaussian processes and Bayesian inference

Lookup NU author(s): Aleksandra Svalova, Dr Peter Helm, Dr Dennis Prangle, Dr Mohamed Rouainia, Professor Stephanie Glendinning, Professor Darren Wilkinson

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

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.


Publication metadata

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

Pages: E12

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

Data Source Location: https://doi.org/10.25405/data.ncl.14331314.v2 https://doi.org/10.25405/data.ncl.14447670.v2


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