Revealing fundamental flexural behavior of reinforced concrete slabs using distributed fiber optic sensors
Bibliographic record
Abstract
Abstract Distributed fiber optic sensors (DFOS) allow for the measurement of distributed strains on concrete surfaces and along steel reinforcement in reinforced concrete (RC) members, and these measurements can quantify reinforcement and concrete behavior. In this investigation, concrete surface and reinforcement strains from DFOS were used to quantify and compare the structural behavior of lightly and moderately reinforced one‐way slabs strips to better characterize localized strain behavior of lightly reinforced RC members with small diameter bars (10 M). By quantifying the entire compression region and reinforcement strain behavior, various structural parameters, such as curvature, strain profiles over the height at various locations, and neutral axis depth were calculated. From the distributed properties, it was determined that significant differences in behavior existed between moderately and lightly reinforced specimens with small diameter bars, with the lightly reinforced specimen displaying non‐uniform behavior along its length. Differences observed in the lightly reinforced member with small diameter bars include local curvature differences both at a crack and between cracks, local evidence of plane sections not remaining plane, possible different internal cracking mechanisms, amongst other local strain behavior differences, which could have implications for future modeling and design of lightly reinforced RC members with small diameter bars.
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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) | 0.000 | 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) | 0.001 | 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".