Rail corrosion forensics using 3D imaging and finite element analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rail infrastructure renewal maintenance is capital intensive. As a contributor to rail deterioration, corrosion damage needs to be accurately analysed for renewal maintenance planning. The main contribution of this study is to introduce an information-dense forensic analysis method for characterizing rail corrosion damage in situ based on 3D imaging. Two state-of-the-art technologies, an arm laser scanner and handheld laser scanner, are employed for onsite digitization of the rail surface. Acquired 3D image data is analysed to characterize pitting corrosion in terms of volume, surface area coverage and average pit depth. Cyclic loading of the sampled rail is simulated using finite element analysis of the 3D image to establish risk potential for crack initiation. A case project was used to validate the feasibility of the developed approach. The results of this study demonstrate the usefulness of applying forensic methodology to renewal maintenance planning.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it