Effect of Precompression and Material Uncertainty on the In-Plane Behavior of URM Pier–Spandrel Systems
Bibliographic record
Abstract
Theoretical and experimental studies on loadbearing masonry walls have shown the significant influence of the axial load level (i.e., precompression) and wall aspect ratio on in-plane lateral resistance. Nonetheless, the impact of the precompression and spatial variability of the material properties needs to be further investigated at the scale of walls with openings. This study presents a stochastic analysis of unreinforced (URM) pier–spandrel systems subjected to both axial loads on piers and lateral loads, considering the spatial variation in material properties. A discontinuum-based computational model was utilized to assess the force–displacement behavior of a benchmark pier–spandrel structure under different vertical precompression levels on piers. A total of 750 simulations were carried out to propagate material uncertainties in lateral load analysis. The proposed modeling strategy, based on the discrete element method, explicitly represents joint openings, sliding, and crushing phenomena at the contact points defined between the adjacent discrete rigid blocks. According to the validated computational modeling strategy, meaningful inferences were made regarding the effect of the precompression level on the maximum displacement and ultimate lateral load-carrying capacity of the benchmark URM pier–spandrel system. The results showed that vertical pressure on piers had considerable influence on the displacement ductility of the system while yielding less variation in the displacement capacity. Furthermore, the appealing feature of the spatial probabilistic analysis is noted in the variation in the lateral load-carrying capacity of the structural system.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".