@article{Yang:2026,
author = {Yang, Y and Ruiz, Lopez A and Tsiampousi, K and Taborda, D},
journal = {Data-Centric Engineering},
title = {A novel adaptive sampling approach with batch selection for the automatic generation of surrogate models in geotechnical engineering},
year = {2026}
}

Download

TY  - JOUR
AB  - 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
AU  - Yang,Y
AU  - Ruiz,Lopez A
AU  - Tsiampousi,K
AU  - Taborda,D
PY  - 2026///
SN  - 2632-6736
TI  - A novel adaptive sampling approach with batch selection for the automatic generation of surrogate models in geotechnical engineering
T2  - Data-Centric Engineering
ER  - 

Download