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Lookup NU author(s): Dr Peter Helm, Dr Aleksandra Svalova, Dr Mohamed Rouainia, Professor Stephanie Glendinning
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
The deterioration of transport infrastructure earthworks is a global problem, with negative impacts for infrastructure resilience, becoming of increasing significance as existing infrastructure ages. Key mechanisms which affect this deterioration include seasonal pore pressure cycling driven by changing weather and climate, and the long-term dissipation of construction induced excess pore pressures. These complex processes lead to significant uncertainty in rates of deterioration and the current state of existing earthworks assets. The objective in this work was to establish a framework to emulate deterministic numerical models of slope deterioration over time using statistical (Gaussian process) emulation. A validated, physically based, deterministic modeling capability has been developed that can replicate the hydro-mechanically coupled behavior of cut and embankment slopes and their deterioration as driven by weather and climate. In parallel, a statistical (Gaussian process) emulator model was developed, and then trained with data from a deterministic modeling parametric study, using a formal experimental design approach, making use of Latin hypercube sampling. Exemplar forecasting outputs are presented to demonstrate application of the approach for use in decision-making. This information can be used in the design of new earthworks and the management of existing earthwork portfolios
Author(s): Helm PR, Svalova A, Morsy AM, Rouainia M, Smith A, El-Hamalawi A, Wilkinson DJ, Postill H, Glendinning S
Publication type: Article
Publication status: Published
Journal: Transportation Geotechnics
Year: 2024
Volume: 44
Print publication date: 01/01/2024
Online publication date: 23/11/2023
Acceptance date: 18/11/2023
Date deposited: 25/11/2023
ISSN (electronic): 2214-3912
Publisher: Elsevier BV
URL: https://doi.org/10.1016/j.trgeo.2023.101155
DOI: 10.1016/j.trgeo.2023.101155
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