Sequential calibration of soil parameters using a two-step surrogate for high-dimensional geotechnical outputs
File(s)
Published version
Author(s)
Yang, Ningxin
Le, Truong
Zdravković, Lidija
Potts, David
Altmeyer, Randolf
Type
Journal Article
Abstract
The observational method enables engineers to update uncertain soil parameters as new monitoring data become available. This process heavily relies on the model’s ability to deliver prompt and reliable results, enabling inverse analysis in geotechnical applications. Surrogate models are commonly used to approximate computationally costly simulations and accelerate calibration. However, the high-dimensional spatial nature of geotechnical outputs complicates the use of traditional surrogate models. To address this challenge for model calibration, this study adopts a type of reduced-order modeling (ROM) approach for high-dimensional outputs. Specifically, a two-step approach is used: (1) dimensionality reduction to extract features from geotechnical outputs and map the original output space into a lower-dimensional space, and (2) constructing a surrogate model directly within the reduced space. This approach improves surrogate model accuracy while maintaining the computational efficiency required. Combined with sequential Bayesian inference, the method is then evaluated for a laterally loaded pile problem. Results demonstrate the method’s effectiveness in handling high-dimensional geotechnical outputs during model calibration.
Date Issued
2026-02-01
Date Acceptance
2025-10-05
Citation
Computers and Geotechnics, 2026, 190
ISSN
0266-352X
Publisher
Elsevier BV
Journal / Book Title
Computers and Geotechnics
Volume
190
Copyright Statement
© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Publication Status
Published
Article Number
107688
Date Publish Online
2025-10-16