Characterization of system sensitivity in SHM event-detection systems
Bibliographic record
Abstract
This paper explores the use of unsupervised neural computation for event detection (ED) in structural health monitoring (SHM) systems. ED techniques are useful in SHM systems for minimizing the size of SHM data sets, and the costs associated with analyzing, transmitting and storing SHM data. The approach to ED explored here is adaptive, self-configuring and does not require detailed information about the structure being monitored. A neural network approach known as frequency sensitive competitive learning (FSCL) is used to model the sensor output of an SHM system. SHM system output states which disagree with the model learned are deemed "novel" and detected as SHM events. The FSCL-ED system is evaluated with SHM data from three structures including the Taylor Bridge, the Portage Creek Bridge and the Golden Boy Statue. Furthermore, this system is able to identify strain gauge events of 0.75, 12.5, 1.25 microstrain or smaller in the SHM measurement data from the Taylor Bridge, the Portage Creek Bridge, and the Golden Boy respectively. The FSCL-ED system is able to identify accelerometer events of .0045g, 0.0020g or smaller in the SHM measurement data from the Portage Creek Bridge, and the Golden Boy respectively. The FSCL-ED system is compared to a simplified event detection (S-ED) system, which does not use power spectral density estimation or unsupervised neural computation. The S-ED system is shown to be effective but less sensitive than the FSCL-ED system to SHM events. As well, the FSCL-ED system is better able to adapt to noisy environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".