Computational investigations on the combined shear–torsion–bending behavior of dry-joint masonry using DEM
Bibliographic record
Abstract
This research explores the mechanical behavior of dry-joint masonry subjected to combined shear–torsion–bending loading via the discrete element method (DEM), which has not been thoroughly investigated in the literature. It also aims to shed light on the accuracy and effectiveness of DEM simulations when the unit–mortar-interfaces are exposed to complex loading scenarios. Throughout this study, the masonry walls are represented as a system of rigid blocks that can mechanically interact with each other via contact points. The proposed modeling strategy is validated against recent experimental findings, and parametric analyses are performed considering the number of contact points and the stiffness. The results reveal that discrete element models can provide accurate predictions when sufficient numbers of contact points are defined on the contact plane. Thus, the required number of contact points to be utilized in the DEM-based simulations is suggested. Furthermore, the dependency of the results on the contact stiffness values, which are associated with vertical pressure, is demonstrated. Tailoring the models based on the suggested discretization ensures capturing the sophisticated stress distributions developing at the joints in masonry structures accurately.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".