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

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 exc...

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Bibliographic Details
Published in:Data-Centric Engineering (Online) 2021-01, Vol.2, Article e12
Main Authors: Svalova, Aleksandra, Helm, Peter, Prangle, Dennis, Rouainia, Mohamed, Glendinning, Stephanie, Wilkinson, Darren J.
Format: Article
Language:English
Subjects:
Bayesian analysis
Clay
Clay soils
Deterioration
emulation
Experiments
Failure times
Gaussian process
Geometry
Infrastructure
Markov chains
Mathematical models
Parameter estimation
Permeability
Pore water pressure
Shear strength
Simulation
Slope stability
Soil permeability
Statistical inference
surrogates
transport
Variables
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Summary: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.
ISSN:2632-6736
2632-6736
DOI:10.1017/dce.2021.14