Damage/Deterioration Detection for Steel Structures Using Distributed Fiber Optic Strain Sensors
Bibliographic record
Abstract
Distributed fiber optic sensors have the potential to be used to detect three critical deterioration mechanisms in steel structures: (1) fatigue cracking, (2) localized damage or deterioration, and (3) distributed damage or deterioration, such as corrosion. This study investigated the strain and spatial resolution of distributed fiber optic sensors and explored the potential benefits and challenges of using distributed fiber optic strain sensors for damage/deterioration detection. The experimental program consisted of a series of axial tension tests performed on steel plate specimens with three types of simulated damage/deterioration: cracking, local cross section reduction, and distributed cross section reduction. The results indicate that similar accuracy to strain gauges can be achieved and distributed fiber optic strain sensors can provide much more detailed information about specimen behavior. The results of a finite-element analysis for each specimen were compared with the experimental measurements. There was good correlation between the two if the boundary conditions were modeled properly. However, care must be taken when selecting the sensing fiber to be used and when interpreting the results.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".