Back analysis of the Old Oak Common box using surrogate models
File(s)
Published version
OA Location
Author(s)
Type
Conference Paper
Abstract
The observational method (OM) has been adopted in urban excavation projects to help manage uncertainties associated with design soil parameters. By deploying comprehensive ground and structural monitoring schemes, the OM can provide a continuous assessment of the progress of the project, with deviations between observed and predicted responses potentially leading to an interruption of construction operations and/or a review of the original design. As part of this process, the OM requires the frequent back analysis of soil parameters using field monitoring data, with the obtained updated soil parameters being subsequently used to simulate future construction stages. This enables the implementation of remedial measures to be evaluated, as well as potential cost saving options to be explored. In this context, the numerical model must reliably capture the actual soil behaviour while remaining computationally efficient, as the back analysis process typically requires many simulations to identify optimal parameter sets. However, such high computational costs are generally incompatible with the constraints imposed by construction timelines and cost
considerations, thereby restricting the feasibility of employing high-fidelity numerical models. To address these challenges, this paper utilises an Artificial Neural Network (ANN) as a surrogate model, capable of predicting results at different construction stages
comparable to those generated by high-fidelity numerical simulations with significantly reduced computational effort, in conjunction with Genetic Algorithms (GA) for automatic back analysis of soil parameters based on field data. The proposed approach for automatic back-analysis is demonstrated through a retrospective case study of the Old Oak Common station in west London (United Kingdom) which is part of the High Speed 2 (HS2) project, highlighting its ability to identify model parameters based on field measurements.
considerations, thereby restricting the feasibility of employing high-fidelity numerical models. To address these challenges, this paper utilises an Artificial Neural Network (ANN) as a surrogate model, capable of predicting results at different construction stages
comparable to those generated by high-fidelity numerical simulations with significantly reduced computational effort, in conjunction with Genetic Algorithms (GA) for automatic back analysis of soil parameters based on field data. The proposed approach for automatic back-analysis is demonstrated through a retrospective case study of the Old Oak Common station in west London (United Kingdom) which is part of the High Speed 2 (HS2) project, highlighting its ability to identify model parameters based on field measurements.
Date Issued
2026-06-14
Date Acceptance
2026-02-13
Citation
Proceedings of the 21st ICSMGE, 2026, pp.3115-3118
ISBN
978-3-9503898-4-5
Publisher
ÖGG, Austrian Society for Geomechanic
Start Page
3115
End Page
3118
Journal / Book Title
Proceedings of the 21st ICSMGE
Copyright Statement
© 2026 The Author(s).
Source
21st International Conference on Soil Mechanics and Geotechnical Engineering (ICSMGE)
Publication Status
Published
Start Date
2026-06-14
Finish Date
2026-06-19
Coverage Spatial
Vienna, Austria