Detection of sub-horizontal flaws in concrete using the synthetic aperture focusing technique
Bibliographic record
Abstract
Concrete deteriorates over time due to environmental changes and/or poor construction processes which can eventually lead to partial or total failure of a structure. Deterioration in concrete manifests itself in different forms such as: freeze and thaw, chemical attack, surface and internal flaws. Concrete and shotcrete linings are widely used as support systems in underground excavations. Surprisingly, a fragmented, damaged shotcrete support system can actually create a less stable environment than the unsupported rock mass. Detection of internal flaws remains a difficult task as they are not always observable on the surface. Yet, the potential to expand and cause damage to the structure is omnipresent. The focus of this work is to locate and characterize two main and common features in concrete structures, (1) sub-horizontal cracks; (2) rock-concrete interfaces. Traditionally, this has been difficult to detect by currently available NDT methods. To obtain high resolution images of cracks in concrete, an extension of the ultrasonic nondestructive technique known as Synthetic Aperture Focusing Technique (SAFT) has been used. However, in order to achieve our research objective, we developed a modified SAFT code in this work. The results of this study demonstrate that the resolving power of our modified 3D SAFT algorithm can provide an accurate profile of both a rock-concrete interface and/or cracks with angles varying from 5 to 15 degrees within concrete slabs having thicknesses of up to twenty centimetres.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".