Semi-Automated Air-Coupled Impact-Echo Method for Large-Scale Parkade Structure
Bibliographic record
Abstract
Structural Health Monitoring (SHM) has moved to data-dense systems, utilizing numerous sensor types to monitor infrastructure, such as bridges and dams, more regularly. One of the issues faced in this endeavour is the scale of the inspected structures and the time it takes to carry out testing. Installing automated systems that can provide measurements in a timely manner is one way of overcoming these obstacles. This study proposes an Artificial Neural Network (ANN) application that determines intact and damaged locations from a small training sample of impact-echo data, using air-coupled microphones from a reinforced concrete beam in lab conditions and data collected from a field experiment in a parking garage. The impact-echo testing in the field is carried out in a semi-autonomous manner to expedite the front end of the in situ damage detection testing. The use of an ANN removes the need for a user-defined cutoff value for the classification of intact and damaged locations when a least-square distance approach is used. It is postulated that this may contribute significantly to testing time reduction when monitoring large-scale civil Reinforced Concrete (RC) structures.
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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".