Development of an inspection system for cracks in a concrete tunnel lining
Bibliographic record
Abstract
Over the last several decades, many concrete tunnels have been constructed for roads, highways, and railways. For safety in concrete tunnels, periodic inspections have been conducted using nondestructive testing technologies and techniques. However, nondestructive tests cannot replace visual inspection because of their slow and complicated procedures. For this reason, their use has been limited to precision inspections. Visual methods of assessment also require significant time commitments, and they produce subjective results regarding measured crack data. This study proposes an inspection system for the rapid measurement of cracks in tunnel linings and provides an objective method for assessing crack data for safety purposes. The system consists of both image data acquisition and analysis systems. The acquisition system takes images with charge-coupled device (CCD) line-scan cameras. The analysis system extracts crack information from the acquired images using image processing. Measured crack information includes the thickness, length, and orientation of cracks. To improve the accuracy of crack recognition, the geometric properties and patterns of cracks in concrete structures should be applied to image processing. This proposed system was verified through a series of experiments in both laboratory and field environments. Key words: crack, inspection, image processing, tunnel lining, tunnel safety.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".