A novel adaptive sampling approach with batch selection for the automatic generation of surrogate models in geotechnical engineering
Author(s)
Yang, Yunxiang
Ruiz Lopez, Agustin
Tsiampousi, Katerina
Taborda, David
Type
Journal Article
Abstract
Surrogate models have gained widespread popularity for their effectiveness in replacing computationally expensive numerical analyses, particularly in scenarios, such as design optimisation procedures, requiring hundreds or thousands of simulations. While one-shot sampling methods – where all samples are generated in a single stage without prior knowledge of the required sample size – are commonly adopted in the creation of surrogate models, these methods face significant limitations. Given that the characteristics of the underlying system are generally unknown prior to training, the adoption of one-shot sampling can lead to suboptimal model performance or unnecessary computational costs, especially in complex or high-dimensional problems. This paper addresses these challenges by proposing a novel, model-independent adaptive sampling approach with batch selection, termed CV-BASHES (Cross-Validation Batch Adaptive Sampling for High Efficiency Surrogates). CV-BASHES is first validated using two analytical functions to explore its flexibility and accuracy under different configurations, confirming its robustness. Comparative studies on the same functions with two state-of-the-art methods, Maxpro and SAS, demonstrate the superior accuracy and robustness of CV-BASHES. Its applicability is further demonstrated through a geotechnical application, where CV-BASHES is used to develop a surrogate model to predict the horizontal deformation of a diaphragm wall supporting a deep excavation. Results show that CV-BASHES efficiently selects training samples, reducing
29 the dataset size while maintaining high surrogate accuracy. By offering more efficient sampling strategies, CV-BASHES streamlines and enhances the process of creating machine learning models as surrogates for tackling complex problems in general engineering disciplines
29 the dataset size while maintaining high surrogate accuracy. By offering more efficient sampling strategies, CV-BASHES streamlines and enhances the process of creating machine learning models as surrogates for tackling complex problems in general engineering disciplines
Date Issued
2026-01-01
Date Acceptance
2025-12-03
Citation
Data-Centric Engineering, 2026, 7
ISSN
2632-6736
Publisher
Cambridge University Press
Journal / Book Title
Data-Centric Engineering
Volume
7
Copyright Statement
© The Author(s), 2026. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial licence (http://creativecommons.org/licenses/by-nc/4.0), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original article is properly cited. The written permission of Cambridge University Press or the rights holder(s) must be obtained prior to any commercial use
License URL
Identifier
10.1017/dce.2025.10036
Subjects
adaptive sampling
batch selection
cross-validation
surrogate models
urban excavations
Publication Status
Published
Article Number
e2
Date Publish Online
2026-01-28