Thermal integrity profiling of concrete piles incorporating three-dimensional defects using deep convolutional neural networks
Author(s)
Sanchez Fernandez, Javier
Ruiz Lopez, Agustin
Taborda, David
Type
Journal Article
Abstract
Thermal Integrity Profiling (TIP) has emerged as a powerful non-destructive method for evaluating cast-in-place concrete piles by monitoring heat of hydration (HoH) temperature profiles using temperature probes embedded in the piles. However, conventional TIP
interpretation relies either on manual inspection of a series of temperature-depth profiles over time, which is inherently prone to subjective bias, or on back-analysis using simplified modelling approaches. This study presents, for the first time, a fully three-dimensional TIP
framework combining Finite Element (FE) simulations with two deep neural networks (NN). A multi-label classifier detects up to three defects per pile, while a regressor quantifies defect volume, location, reinforcement-cage displacement, and hydration parameters. A database with hydration temperature profiles from 19,445 simulations encompassing randomised pile lengths, soil stratigraphies, cage misalignments and multi-defect configurations, is used to train the two NNs. The classifier achieves a per-defect accuracy of 93.6% (96.8% per sample). The
regression model achieves near-perfect accuracy for cage displacement (R² = 0.992) and defect volume (mean absolute error of 0.03 m³), and an adjusted R² of 0.92 for defect location. This novel automated approach significantly enhances the objectivity, test speed, and reliability of TIP-based defect detection across a wide range of deep pile foundations.
interpretation relies either on manual inspection of a series of temperature-depth profiles over time, which is inherently prone to subjective bias, or on back-analysis using simplified modelling approaches. This study presents, for the first time, a fully three-dimensional TIP
framework combining Finite Element (FE) simulations with two deep neural networks (NN). A multi-label classifier detects up to three defects per pile, while a regressor quantifies defect volume, location, reinforcement-cage displacement, and hydration parameters. A database with hydration temperature profiles from 19,445 simulations encompassing randomised pile lengths, soil stratigraphies, cage misalignments and multi-defect configurations, is used to train the two NNs. The classifier achieves a per-defect accuracy of 93.6% (96.8% per sample). The
regression model achieves near-perfect accuracy for cage displacement (R² = 0.992) and defect volume (mean absolute error of 0.03 m³), and an adjusted R² of 0.92 for defect location. This novel automated approach significantly enhances the objectivity, test speed, and reliability of TIP-based defect detection across a wide range of deep pile foundations.
Date Issued
2026-06-22
Date Acceptance
2026-06-15
Citation
Geomechanics and Geoengineering, pp.1-16
ISSN
1748-6025
Publisher
Taylor and Francis Group
Start Page
1
End Page
16
Journal / Book Title
Geomechanics and Geoengineering
Copyright Statement
© 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
License URL
Identifier
10.1080/17486025.2026.2691953
Subjects
Thermal integrity profiling
defect detection
machine learning
concrete piles
finite element modelling
Publication Status
Published online
Date Publish Online
2026-06-22