Damage detection and characterization using fiber optic sensors
Bibliographic record
Abstract
Fiber optic sensors (FOS) have significantly evolved and have reached their market maturity during the last decade. Their widely recognized advantages are high precision, long-term stability, and durability. But in addition to these advantageous performances, FOS technologies allow for affordable instrumentation of large areas of structure enabling global large-scale monitoring based on long-gauge sensors and integrity monitoring based on distributed sensors. These two approaches are particularly suitable for damage detection and characterization, i.e., damage localization and to certain extent quantification and propagation, as illustrated by two applications presented in detail in this paper: post-tensioned concrete bridge and segmented concrete pipeline. Early age cracking was detected, localized and quantified in the concrete deck of a pedestrian bridge using embedded long-gauge FOS. Post-tensioning of deck closed the cracks; however, permanent weakening in a bridge joint occurred due to cracking and it was identified and quantified. The damage was confirmed using embedded distributed FOS and a separate load test of the bridge. Real-size concrete pipeline specimens and surrounding soil were equipped with distributed FOS and exposed to permanent ground displacement in a large-scale testing facility. Two tests were performed on different pipeline specimens. The sensors bonded on the pipeline specimens successfully detected and localized rupture of pipeline joints, while the sensors embedded in the soil were able to detect and localize the failure plane. Comparison with strain-gauges installed on the pipeline and visual inspection after the test confirmed accurate damage detection and characterization.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".