Macro-Mesoscale Submodeling Approach for Analysis of Large Masonry Structures
Bibliographic record
Abstract
In this work, a sub-modeling technique is proposed for the analysis of large-scale masonry structures. The approach couples an anisotropic macroscale formulation, derived by incorporating the notion of a fabric tensor for an orthotropic material, with mesoscale analysis. The latter employs distinct inelastic constitutive relations assigned to the brick material and brick-mortar interfaces, which enable the tracing of localized damage propagation. The mechanical properties at the macro-level are identified from the ‘virtual’ set of data generated through mesoscale analysis, ensuring consistency between the two approaches in representing the masonry material across different scales. In the numerical analysis, the macroscale approach is first applied over the entire domain to interpolate the kinematic boundary conditions in a local region of interest, which is then re-analyzed based on the mesoscale framework. The developed strategy is illustrated by simulating the shear response of a large-scale unreinforced masonry wall with multiple window openings.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".