Computational Modelling of Damage Progression in Unreinforced Masonry Walls via DEM
Bibliographic record
Abstract
Unreinforced masonry (URM) walls are the common load-bearing elements for old masonry buildings and heritage structures. As witnessed from the past and recent earthquakes, URM walls may demonstrate various collapse mechanisms along with different crack patterns influenced by the wall aspect ratio, vertical pre-compression load, opening size and ratio, among many other factors. Typically, the mortar joints and unit-mortar interfaces are the weak planes where we expect to observe most failures, such as sliding, cracking and joint opening. However, it is not a straightforward task to simulate the structural behaviour and the failure mechanism of URM walls, including the crack localizations and propagation through the mortar joints, using the standard continuum-based computational models given the composite and highly nonlinear nature of the material. In this context, the present research offers a discontinuum-based approach to simulate the damage progression in URM walls subjected to combined shear-compression loading using the discrete element method (DEM). The masonry walls are represented via distinct elastic blocks interacting through point contacts to their surroundings. It is aimed to present the effect of the local fracture mechanism on the macro response of the masonry walls via validated DEM-based numerical models that can address all possible fracture mechanisms occurring at the unit-mortar interfaces. An innovative damage monitoring technique relying on the stress state at the point contacts is implemented and utilized to explore the associated damage progression in URM walls. The results show the great potential of the adopted modelling strategy to better understand the mechanics of URM walls and indicate the effect of strength properties of masonry constituents on the overall in-plane capacity of the load-bearing walls.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".