Lateral Capacity of URM Walls: A Parametric Study Using Macro and Micro Limit Analysis Predictions
Bibliographic record
Abstract
This research investigates the texture influence of masonry walls’ lateral capacity by comparing analytical predictions performed via macro and micro limit analysis. In particular, the effect of regular and quasi-periodic bond types, namely Running, Flemish, and English, is investigated. A full factorial dataset involving 81 combinations is generated by varying geometrical (panel and block aspect ratio, bond type) and mechanical (friction coefficient) parameters. Analysis of variance (ANOVA) approach is used to investigate one-way and two-way factor interactions for each parameter in order to assess how it affects the horizontal load multiplier. Macro and micro limit analysis predictions are compared, and the differences in terms of mass-proportional horizontal load multiplier and failure mechanism are critically discussed. Macro and micro limit analysis provide close results, demonstrating the reliability of such approaches. Furthermore, results underline how the panel and block aspect ratio had the most significant effect on both the mean values and scatter of results, while no significant effect could be attributed to the bond types.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".