Automated Measuring and Tracking the Deformations of Concentrically Braced Frames
Bibliographic record
Abstract
Deformation is a critical indicator required to evaluate the structural safety, stability, and integrity.For example, the deformed shape of a building structure is a key parameter that needs to be measured and tracked during laboratory testing or post-disaster inspections.However, existing techniques used for the deformation measurement and tracking do not have all the desirable characteristics, such as: reliability, accuracy, low-cost and simplicity of tools installation.In order to address this issue, a novel method has been proposed in this study.The method consists on using the image processing technique to automatically measure and track the deformations of concentrically braced frame (CBF) structures.Accordingly, the critical points in a CBF structure are first identified and the topological configuration of the structure is retrieved.The points and topological configuration characterize the basic shape of the structure.In this way, the deformation of the structure can be determined by measuring and tracking these points and their topological configuration.To show its effectiveness, the proposed method has been tested against the manual deformation measurements.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 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".