Breaking boundaries: discontinuum failure analysis of dry-joint masonry using physics engine models
Bibliographic record
Abstract
Discontinuum approaches, including the Distinct Element Method (DEM), are well-established for simulating the failure of dry-joint unreinforced masonry (URM) structures, particularly under in-plane (IP) shear-compression and out-of-plane (OOP) loading. However, they may be computationally intensive, with building-scale DEM analyses requiring up to 3 days for 15 s of seismic loading. This paper breaks boundaries between structural engineering and computer science by presenting the first systematic evaluation of PyBullet, an open-source physics engine based on Bullet Physics – originally conceived for visually plausible virtual animations – for simulating the mechanical response and collapse of dry-joint URM assemblies at different scales. Leveraging PyBullet’s rigid body algorithms, contact models, and efficient constraint solvers, 3D simulations were performed for IP shear-compression walls, settlement-induced damage in interlocking panels, and OOP tilting of URM. Results were benchmarked against experimental data and established discontinuum models – PyBullet predicted peak loads within + 16 % of DEM for IP shear-compression walls. Numerical stability was maintained with time steps in the order of 0.001 s, and full simulations completed within 5 min – up to 6 times faster than DEM. OOP tilting analyses reproduced expected collapse modes (diagonal cracking, overturning) with critical collapse angles underestimated by up to 32 %, largely due to premature block slippage linked to contact stiffness and friction force coupling. Settlement-induced failure in interlocking panels showed good agreement with experimentally observed failure patterns, with ultimate displacements within ±3 % for non-interlocking cases. The study demonstrates that PyBullet offers a computationally efficient alternative for dry-joint URM analysis, providing reduced runtimes and acceptable predictive accuracy, especially for preliminary or large-scale probabilistic assessments. Further refinement of contact stiffness calibration strategies would enhance predictive consistency, supporting the adoption of physics engines as viable alternatives to conventional discontinuum methods for rapid masonry collapse and debris simulations. • New application of physics engines (PyBullet) to structural analysis of dry-joint URM. • Quasi-static IP and OOP simulations validated with DEM and experimental results. • PyBullet results comparable with DEM counterparts, at lower computational cost. • PyBullet is a more efficient and reliable alternative to DEM for dry-joint URM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".