Equivalent frame discretisation for URM façades with irregular opening layouts
Bibliographic record
Abstract
Abstract Researchers and practitioners widely employ simplified Equivalent Frame Models (EFM) for reproducing the in-plane governed response of unreinforced brick masonry (URM) structures, as they typically represent an acceptable compromise between numerical accuracy and computational cost. However, when considering URM structural systems with irregular opening distribution, the definition of the effective height and length of deformable components (i.e. pier and spandrel elements) still represents an open challenge. In this work, the influence of irregular distribution of openings on the predicted lateral response of full-scale URM façades was investigated. To this end, several geometrical combinations characterised by various degrees of irregularity were considered and idealised according to commonly employed EF discretisation approaches. Then, after a preliminary calibration process against experimental tests on both individual piers and a full-scale building façade, EFM results were compared with micro-modelling predictions, carried out within the framework of the Applied Element Method and used as a benchmark. Although in specific irregular configurations using some discretisation approaches, macro and micro-models converge to similar results, non-negligible differences in terms of initial lateral stiffness, base-shear and damage distribution were observed with other EF schemes or opening layouts, thus indicating that a careful selection of appropriate criteria is indeed needed when performing in-plane analyses of URM systems with irregular opening distributions. Finally, building on inferred simulated data, potential solutions are given to overcome typical EF discretisation issues and better approximate micro-modelling outcomes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".