Detection of bond failure in the anchorage zone of reinforced concrete beams via acoustic emission monitoring
Bibliographic record
Abstract
Abstract In this study, acoustic emission (AE) monitoring was utilised to identify the onset of bond failure in reinforced concrete beams. Beam anchorage specimens were designed and tested to fail in bond in the anchorage zone. The specimens included four 250 × 250 × 1500 mm beams with four variable bonded lengths (100, 200, 300, and 400 mm). Meanwhile, an additional 250 × 250 × 2440 mm beam, with 200 mm bonded length, was tested to investigate the influence of sensor location on the identification of bond damage. All beams were tested under four-point loading setup and continuously monitored using three distributed AE sensors. These attached sensors were exploited to record AE signals resulting from both cracking and bond deterioration until failure. The variations in the number of AE hits and cumulative signal strength (CSS) versus test time were evaluated to achieve early detection of crack growth and bar slippage. In addition, AE intensity analysis was performed on signal strength of collected AE signals to develop two additional parameters: historic index ( H ( t )) and severity ( S r ). The analysis of these AE parameters enabled an early detection of both first cracks (at almost the mid-span of the beam) and bar slip in either of the anchorage zones at the beams’ end before their visual observation, regardless of sensor location. The results also demonstrated a clear correlation between the damage level in terms of crack development/measured free end bar slip and AE parameters (number of hits, CSS, H ( t ), and S r ).
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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 | 0.000 | 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 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".