Comprehensive Condition Assessment of Dam Crest Structures Using Multi-Method Non-Destructive Evaluation Techniques
Bibliographic record
Abstract
This paper presents a comparative analysis of a multi-modal non-destructive testing and evaluation program in evaluating the condition of concrete elements in two dam structures in Quebec, Canada. For each dam structure, one area, exhibiting varying degrees of deterioration, were meticulously investigated to evaluate the surface and subsurface integrity of the concrete. Visual surveys revealed surface-level defects, including cracks, spalling, and evidence of previous repairs. Subsurface conditions were evaluated through a multi modal non-destructive evaluation program. Ground-Penetrating Radar (GPR) was used for rapid screening of the area for potential delamination and voids, particularly in areas with prior repairs. Ultrasonic pulse echo (UPE) was utilized to verify the findings of the GPR scans, providing high-resolution data on internal defects within the top 500 mm. In addition, Impulse-Response (IR) testing was deployed to affirm presence of potential anomalies (voids and delamination) by identifying high-mobility zones. Spectral Analysis of Surface Waves (SASW) was employed to evaluate mechanical properties, revealing variations in shear wave velocity, with lower velocities correlating to compromised material integrity. The main objective of the study was to study the effectiveness of these NDT-E methods in identifying sub-surface anomalies. The findings of this study revealed a good agreement between among between different assessment methods, enhancing confidence in defect detection and material characterization. While much of the concrete was found to be in satisfactory condition, several areas especially those with prior repairs exhibited notable surface and subsurface degradation. The findings emphasize the need for targeted maintenance and repair strategies to mitigate the identified defects and ensure the long-term safety and performance of the dams. This study showed the efficacy of combining multiple NDE techniques for detailed, non-invasive assessments of dam 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".