A DEM-based modelling framework to investigate fracture processes in masonry
Bibliographic record
Abstract
Construction materials used for masonry units and mortar are typically quasi-brittle. The current research presents a computational framework to simulate fracture in these materials using the discrete element method. In this framework, the surface geometry is first discretized into a fine unstructured triangular tessellation, which is then extruded to form triangular blocks that constitute the 3D volume. The fine discretization enables modelling of the initiation, propagation and coalescence of cracking within the simulated materials. The ability of the proposed modelling approach to reproduce realistic fracture patterns is first evaluated using experimental results from the literature. The comparisons demonstrate that the proposed framework can be used to simulate Mode-I fractures in direct tension as well as mixed mode fractures under compression. The influence of block size on the crack pattern and global load–displacement predictions is systematically explored. The framework is then utilized to investigate the influence of pre-existing morphological irregularities on the fracture response. For this purpose, a custom algorithm is developed to introduce joined element clusters (to simulate stiff inclusions within the material) and voids (to simulate defects within the material) within the geometry. Results show that defects within the material, particularly in masonry mortar joints, act as crack initiators and significantly reduce load-carrying capacity, underscoring the importance of mortar integrity in structural performance. The examples highlight the framework’s potential to enhance the performance evaluation of masonry materials and 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".