In this section
@article{Sanchez:2026:10.1080/17486025.2026.2691953,
author = {Sanchez, Fernandez J and Ruiz, Lopez A and Taborda, D},
doi = {10.1080/17486025.2026.2691953},
journal = {Geomechanics and Geoengineering},
title = {Thermal integrity profiling of concrete piles incorporating three-dimensional defects using deep convolutional neural networks},
url = {http://dx.doi.org/10.1080/17486025.2026.2691953},
year = {2026}
}
TY - JOUR
AB - 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.
AU - Sanchez,Fernandez J
AU - Ruiz,Lopez A
AU - Taborda,D
DO - 10.1080/17486025.2026.2691953
PY - 2026///
SN - 1748-6025
TI - Thermal integrity profiling of concrete piles incorporating three-dimensional defects using deep convolutional neural networks
T2 - Geomechanics and Geoengineering
UR - http://dx.doi.org/10.1080/17486025.2026.2691953
ER -
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