Classification of novel events for structural health monitoring systems
Bibliographic record
Abstract
This article reports on results obtained when applying neural networks to the problem of vehicle classification from SHM measurement data. It builds upon previous work which addressed the issue of reducing vast amounts of data collected during an SHM process by storing only those events regarded as being "interesting," thus decreasing the stored data to a manageable size. This capability is extended here by providing a means to group and classify these novel events using artificial neural network (ANN) techniques. Two types of neural systems are investigated, the first one consists of two neural layers employing both supervised and unsupervised learning. The second, which is an extension of the first, has a data pre-processing stage. In this later system, input data presented to the system is first pre-scaled before being presented to the first network layer. The scaling value is retained and later passed to the second layer as an extra input. The results obtained for vehicle classification using these two methods showed a success rate of 60% and 90% for the first and second ANN systems respectively.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".