Concrete deterioration detection using distributed sensors
Bibliographic record
Abstract
The potential for using a distributed fibre optic strain sensor system to detect and quantify localised reinforcement deterioration and explore the failure mechanism of reinforced concrete beams is investigated. The objective is to discover whether distributed fibre optic strain sensors installed either internally or externally can detect pitting corrosion in reinforced concrete beams, and be used to better understand structural behaviour. These sensors enable strain to be measured along the full length of the fibre optic cable, whereas conventional electrical resistance strain gauges offer discrete measurements that may not capture localised strain changes. Axial tension tests on reinforcement bars demonstrate that deterioration detection is possible, but that quantification of deterioration is not necessarily straightforward, depending on the extent of deterioration. Four-point bending tests on reinforced concrete beams illustrate that localised deterioration can be detected and quantified with embedded sensing fibres and that cracks can be detected using externally bonded fibres. The top and bottom reinforcement strain profiles are used to demonstrate that the unexpected ultimate failure loads of the beams are the result of specimens behaving as tied arches at failure. Overall the distributed fibre optic sensor system proves to be effective for detection and quantification of localised deterioration and provides the necessary data to assess the beam failure mechanism.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".