Transferring vision-based data to discontinuum analysis for the assessment of URM walls
Bibliographic record
Abstract
This research presents a new workflow to assess unreinforced masonry (URM) walls based on vision-based data. The morphological features (i.e. brick pattern) of URM walls are obtained through high-resolution orthogonal images and then imported into a three-dimensional discrete element code (3DEC) to perform structural analysis via a semi-automatic procedure. URM walls are represented using rigid blocks that can mechanically interact along their boundaries. The mechanical interaction is simulated based on the point-contact hypothesis considering spring frictional elements. Once the numerical approach is validated using the results available in the literature, the proposed data-driven modelling strategy is applied to different URM wall cross-sections, with or without openings. The results show that the adopted semi-automatic workflow can be a fast and robust solution to simulate the structural behaviour of existing URM buildings by considering their construction techniques efficiently.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".