Development of neural network-based surrogate models in geotechnics: a state-of- the-art review
Citation Link: https://doi.org/10.15480/882.18800
Publikationstyp
Journal Article
Date Issued
2026-06-08
Sprache
English
TORE-DOI
Volume
2
Issue
1
Start Page
135
End Page
152
Citation
Machine Learning and Data Science in Geotechnics 2 (1): 135–152 (2026)
Publisher DOI
Publisher
Emerald
Purpose -This state-of-the-art paper provides an overview of surrogate modelling methods suitable for geotechnical engineering, covering the entire cycle of model development.
Design/methodology/approach -The paper discusses neural network-based surrogate modelling and its applicability in geotechnical engineering, including its limitations. It addresses techniques for data set generation, sampling, tuning, selecting activation functions and assessing accuracy
Findings - Each step during the development of surrogate models offers various options and therefore requires a systematic approach. Besides standardised benchmarking and richer data sets key areas for future research include advanced or hybrid architectures, such as physics-informed long short-term memory networks and transformers to improve generalisability, efficiency and physical consistency.</jats:p>
Originality/value -This paper reviews and highlights the most promising methods for surrogate model development in geotechnical engineering, considering the most critical aspects for building high-performing predictive models and lays an outlook for future research.
Design/methodology/approach -The paper discusses neural network-based surrogate modelling and its applicability in geotechnical engineering, including its limitations. It addresses techniques for data set generation, sampling, tuning, selecting activation functions and assessing accuracy
Findings - Each step during the development of surrogate models offers various options and therefore requires a systematic approach. Besides standardised benchmarking and richer data sets key areas for future research include advanced or hybrid architectures, such as physics-informed long short-term memory networks and transformers to improve generalisability, efficiency and physical consistency.</jats:p>
Originality/value -This paper reviews and highlights the most promising methods for surrogate model development in geotechnical engineering, considering the most critical aspects for building high-performing predictive models and lays an outlook for future research.
Subjects
Artificial intelligence
Neural networks
Surrogate modelling
Computational geotechnics
Sustainable development
DDC Class
624.1: Structural Engineering
006.3: Artificial Intelligence
Publication version
publishedVersion