Coupled elasto-softening contact models in DEM to predict the in-plane response of masonry walls
Bibliographic record
Abstract
This study explores the effect of the local fracture mechanisms of unreinforced masonry (URM) walls on their macro-behavior through different contact models implemented within the discrete element method (DEM). The brittle contact constitutive laws are the standard choice for simulating block interaction in the literature when the discontinuum-based analysis of masonry structures is performed using DEM. However, this assumption may yield inaccurate predictions related to the pre- and post-peak behavior of brick or stonework assemblages subjected to combined compression–shear loading. To this end, tension–shear and shear–compression coupled elasto-softening contact models are proposed to simulate crack propagation and collapse mechanism phenomena in masonry structures, providing a more accurate prediction of the force–displacement response of URM walls. Throughout the study, the discontinuous nature of masonry composite is modeled via discrete blocks that can interact with each other along their boundaries based on the point contact hypothesis. Once the adopted modeling strategy is validated, its performance is demonstrated by comparing it against the commonly used brittle contact model and other available computational approaches. A comprehensive sensitivity analysis is performed on various essential parameters characterizing the post-peak behavior of contact models, and significant inferences are made. Finally, the proposed contact models have shown to be better alternatives to existing brittle and uncoupled elasto-softening contact models used for the DEM-based simulation of masonry structures.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".