NDT-based Condition Assessment of Concrete Tanks in Mine Facilities
Bibliographic record
Abstract
This paper presents a comprehensive condition assessment of two large reinforced concrete tanks at a mining facility in Canada, using a multi-method approach. The program combined digital inspection through LiDAR and photographic mapping, Non-Destructive Evaluation (NDT-E) methods including Ultrasonic Pulse Echo (UPE), Ground Penetrating Radar (GPR), and Rebound Hammer (RH), along with intrusive testing (core sampling). In addition to laboratory analysis of the core samples, complementary non-destructive tests, including Surface Electrical Resistivity (SER), cross-core Ultrasonic Pulse Velocity (UPV), were carried out on the same core samples. Results showed that Tank 1 had generally good concrete quality with few localized anomalies, while the Tank 2 exhibited more minor internal irregularities but maintained high UPV and low permeability values. Compressive strengths exceeded design requirements, and petrographic studies identified minor ASR and sulfate exposure. This integrated assessment demonstrates the value of combining multiple NDT-E and intrusive techniques to achieve a thorough evaluation, support targeted maintenance, and ensure the long-term serviceability of critical infrastructure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".