Simplified Micro-Modeling of a Masonry Cross-Vault for Seismic Assessment Using the Distinct Element Method
Bibliographic record
Abstract
The assessment of the seismic performance of unreinforced masonry cross-vaults is still a challenge in numerical analysis, due to complex curved geometries and bond patterns, and uncertainties related to the selection of adequate modeling strategies, including but not limited to that of material properties, damping scheme, and unit/joint idealization. This paper presents the results of a collaborative effort to validate, against the shake table test of both unstrengthened and strengthened masonry cross-vault specimens as part of the SERA Project Blind Prediction and Post-diction Competition, various discontinuum-based numerical approaches. First, the geometry of the cross-vault is created using a Python-based computational framework to accurately represent the brick arrangement and the shape of the vault. Then, the geometry is converted into an assemblage of deformable blocks and analyzed using the Distinct Element Method (DEM). An elasto-softening contact model based on fracture energy is implemented in the masonry joints to simulate crushing, tensile, and shear failures. The performance of the proposed strategy, conceived for the unstrengthened configuration of the tested vault specimen and then adapted to include the presence of cementitious repairs, shows satisfactory agreement with both qualitative and quantitative experimental responses, also revealing critical insights and lessons learned through the blind/post-prediction exercise.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".