Vibration-based damage identification for reinforced concrete slab-type structures using fiber-optic sensors and random decrement technique
Bibliographic record
Abstract
This paper presents and evaluates a damage identification system for reinforced concrete (RC) slab-type structures based on non-destructive vibration testing, Random decrement (RD) signal processing technique, and embedded smart network of fiber-optic sensors. The proposed system aims to overcome the challenges associated with the use of electrical sensors and signal processing of noisy dynamic data. Two experimental modal analysis investigations have been conducted. First modal testing focuses on investigating the capability of fiber-optic sensors and Multi-channel random decrement (MCRD) processing technique to locate damage in RC slabs through changes in the first mode shape response with damage. The second modal testing focuses on the detection of damage intensity using the RD technique through the change in frequency and damping dynamic parameters. The results show that RD technique can be used effectively to extract the free vibration response of RC slab-type structures; fiber-optic sensors are more sensitive to capture damage severity in comparison to electrical accelerometer sensors, especially, at steel yielding and failure load; MCRD technique can be used effectively to generate mode shapes for RC slabs based on fiber-optic grating FBG sensors measurements. On the other hand, electrical strain gauges were noisy and it was difficult to obtain any measurable data; A damage identification system based on non-destructive vibration testing, MCRD processing technique, and using an embedded smart network of fiber-optic sensors can estimate accurately the damage location through changes in the first mode shape.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".