Thermal Integrity Profiling of Cast-in-Place Piles with Defects: Insights from Numerical Modeling
Bibliographic record
Abstract
Thermal integrity profiling (TIP) is one of the newest and most promising methods for assessing the integrity of cast-in-place concrete piles. Despite its growing application, TIP research remains limited. Current practices generally rely on nominal values, such as unchangeable default values in commonly used TIP software, for the thermal properties of soil and concrete, without sensitivity analyses that account for the wide range of possible values. This study demonstrates that using default values, instead of measured or variable values, can result in inaccurate interpretation of anomaly. Furthermore, there is limited knowledge regarding the relative importance of input parameters in TIP analysis, and research on advanced TIP modeling under multilayered soil conditions is scarce. To address these technical challenges, this study introduces a new modeling approach that includes a custom subroutine in Abaqus for simulating fully nonlinear, element-by-element time- and temperature-dependent heat generation from cement hydration. This finite-element (FE) model was validated against previous experimental data. Through a parametric study and analysis of variance on 70 FE pile models with predefined defects, the research identified concrete specific heat as the most critical input parameter for TIP, followed by concrete and soil thermal conductivity. It was inferred that ignoring the effect of layered soil can lead to false interpretation of defects. A flowchart was proposed to enhance accuracy in TIP interpretation and anomaly detection in practical applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".