Effect of Opening Size in Unreinforced Masonry Walls Subjected to Lateral Loads: Computational Modeling and Code Comparison
Bibliographic record
Abstract
Perforated unreinforced masonry (URM) walls are used in most existing masonry buildings as structural and nonstructural elements. Depending on the size and position, openings may detrimentally affect the stiffness and seismic capacity of URM walls. This research investigates the structural behavior of perforated URM walls with different opening sizes and proportions subjected to lateral loading using the discrete element method (DEM). In the applied modeling strategy, masonry walls are composed of rigid blocks, where their mechanical interactions are simulated via point-contact hypotheses. Once the numerical approach is validated, parametric analyses are performed to better understand the effect of different opening sizes and their aspect ratios on the failure mechanism and shear capacity of perforated URM walls. The results quantify the lateral load–carrying capacity and demonstrate its inverse relationship with the opening size. Furthermore, a slight influence of contact stiffness (varied from 10 to 120 GPa/m) on the ultimate lateral load in DEM-based simulations is noted. Finally, the obtained shear force capacities are compared against the strength prediction equations provided in current US standards. In most cases, the predictions of the US standard provide conservative values relative to the DEM results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".