Data-driven surrogates for predicting thermal performance and response functions of thermo-active piles
File(s)
Published version
Author(s)
Sanchez Fernandez, Javier
Ruiz Lopez, Agustin
Taborda, David
Type
Journal Article
Abstract
Purpose
This paper develops fast, accurate data-driven surrogate models to predict the thermal performance and thermal response functions of single thermo-active piles. These replace computationally expensive finite element simulations and geometry-specific g-function calculations with generalisable machine-learning models suitable for preliminary design and performance assessment. This study aims to improve accessibility, reduce computational cost and support wider adoption of thermo-active piles within low-carbon and net-zero infrastructure strategies.
Design/methodology/approach
Two Artificial Neural Network surrogates were trained on databases generated from 3D transient finite element simulations of thermo-active piles. One surrogate predicts transient power output per unit length under a prescribed inlet fluid temperature, while the second predicts normalised pile wall and outlet thermal responses under constant heat flux. Input parameters were sampled using Latin Hypercube sampling. Model training used feature normalisation, cross-validation and regularisation, with performance evaluated using standard regression metrics and SHAP-based interpretability analysis.
Findings
Both surrogate models demonstrate excellent predictive accuracy and strong generalisation. The power output surrogate achieves R² values exceeding 0.99 with mean absolute errors typically below 2 W/m for most of the operational period. The thermal response surrogate reproduces pile wall and outlet g-functions over 10 years with global per-point R² values of 0.994–0.997 and per-timestep averages of 0.971 (wall) and 0.959 (outlet). Validation against a field thermal response test confirms reliable extrapolation beyond the trained diameter range. Computational time is reduced by several orders of magnitude compared with finite element analysis.
Originality/value
This study presents the first generalisable surrogate framework capable of predicting both power output per unit length and pile-specific g-functions for single thermo-active piles across a wide parameter space. By combining 3D numerical simulations with machine-learning surrogates and interpretability analysis, the work bridges the gap between physics-based modelling and practical engineering design. The approach provides a computationally efficient alternative to traditional thermo-active pile design methods.
This paper develops fast, accurate data-driven surrogate models to predict the thermal performance and thermal response functions of single thermo-active piles. These replace computationally expensive finite element simulations and geometry-specific g-function calculations with generalisable machine-learning models suitable for preliminary design and performance assessment. This study aims to improve accessibility, reduce computational cost and support wider adoption of thermo-active piles within low-carbon and net-zero infrastructure strategies.
Design/methodology/approach
Two Artificial Neural Network surrogates were trained on databases generated from 3D transient finite element simulations of thermo-active piles. One surrogate predicts transient power output per unit length under a prescribed inlet fluid temperature, while the second predicts normalised pile wall and outlet thermal responses under constant heat flux. Input parameters were sampled using Latin Hypercube sampling. Model training used feature normalisation, cross-validation and regularisation, with performance evaluated using standard regression metrics and SHAP-based interpretability analysis.
Findings
Both surrogate models demonstrate excellent predictive accuracy and strong generalisation. The power output surrogate achieves R² values exceeding 0.99 with mean absolute errors typically below 2 W/m for most of the operational period. The thermal response surrogate reproduces pile wall and outlet g-functions over 10 years with global per-point R² values of 0.994–0.997 and per-timestep averages of 0.971 (wall) and 0.959 (outlet). Validation against a field thermal response test confirms reliable extrapolation beyond the trained diameter range. Computational time is reduced by several orders of magnitude compared with finite element analysis.
Originality/value
This study presents the first generalisable surrogate framework capable of predicting both power output per unit length and pile-specific g-functions for single thermo-active piles across a wide parameter space. By combining 3D numerical simulations with machine-learning surrogates and interpretability analysis, the work bridges the gap between physics-based modelling and practical engineering design. The approach provides a computationally efficient alternative to traditional thermo-active pile design methods.
Date Issued
2026-12-14
Date Acceptance
2026-06-22
Citation
Machine Learning and Data Science in Geotechnics, 2026, 2 (1), pp.173-185
ISSN
3029-0422
Publisher
Emerald Publishing
Start Page
173
End Page
185
Journal / Book Title
Machine Learning and Data Science in Geotechnics
Volume
2
Issue
1
Copyright Statement
© Javier Sánchez Fernández, Agustín Ruiz Lopez and David Taborda. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/
License URL
Identifier
10.1108/MLAG-01-2026-0002]
Subjects
Energy geotechnics
Thermo-active structures
Ground source energy
Thermal performance
Numerical analysis
Surrogate models
Artificial neural networks
Publication Status
Published
Date Publish Online
2026-08-11