Crack Detectability and Durability of Coaxial Cable Sensors in Reinforced Concrete Bridge Applications
Bibliographic record
Abstract
The working mechanism and the measurement principle of topology-based crack sensors made of coaxial cables are briefly reviewed. The sensitivity, spatial resolution, and ruggedness of two coaxial cable sensors, respectively made of rubber and Teflon dielectric materials, were compared and validated with laboratory testing of a 4/5-scale, T-shaped, reinforced concrete beam-column specimen. Two Teflon sensors were installed on one of the solid decks of a three-span continuous highway bridge to investigate their durability and measurement repeatability. Laboratory tests indicated that both types of sensors have high sensitivity, but the Teflon sensor has a higher spatial resolution and a negligible spillover effect of any significant cracks. At a 90-degree bend, however, the Teflon sensor is more susceptible than the rubber sensor to the rubbing action of the outer conductor of a coaxial cable against its dielectric layer. No cracks were observed during the field load tests of the instrumented bridge. Both sensors indicated high durability in real-world application but a certain variation of waveforms was measured over a period of 5 years because of the use of different instruments. Future research is directed to develop an online calibration of crack sensors with a small portion of built-in standard cable at the end of the cable sensor.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 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".