Automated scaffolding safety analysis: strain feature investigation using support vector machines
Why this work is in the frame
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Bibliographic record
Abstract
This study developed a methodology that can use real-time strain data for the assessment of scaffolding safety conditions. The researchers identified 23 safety cases of individual member failure with generic global failure for a four-bay, three-story scaffold model and used scaffold member strain values to identify potential failure cases. A computer simulation on the scaffold model generated the strain datasets required for classification with a support vector machine (SVM). The SVM classification demonstrated a stable prediction accuracy after training with a certain number of strain datasets. Furthermore, the 2nd order polynomial kernel function resulted in better prediction compared to other SVM kernel functions. These results imply that the real-time assessment of scaffolding structures is possible with a limited number of training data for machine-learning classification.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it