Nonlinear Seismic Assessment of a Historic Rubble Masonry Building via Simplified and Advanced Computational Approaches
Bibliographic record
Abstract
This research presents a comprehensive nonlinear quasi-static seismic assessment of an unreinforced rubble masonry building, Bytown Museum in Ottawa, Canada, using discontinuum-based analyses. In the proposed modeling approach, non-uniform geometrical properties of rubble masonry walls are replicated via a group of rigid polyhedral blocks interacting along their boundaries based on the discrete element method (DEM). Once the adopted modeling strategy is validated, the nonlinear quasi-static analysis of the South and North façades of the Bytown Museum is performed. Special attention is given to the irregular block generation within the discontinuum analysis framework, where discrete element models are generated from high-resolution site recording data, representing the masonry morphology at a high level of detail. Then, the predicted collapse mechanisms from advanced computational models are further utilized to generate pre-defined macro-blocks in kinematic limit analyses, providing a simple alternative solution for seismic assessment. The results reveal the significant effect of openings and the construction technique (morphology) in unreinforced rubble masonry buildings that can play an important role in the structural capacity and behavior. Moreover, it is noted that DEM-based solutions provide lower seismic capacity compared to kinematic limit analyses. Finally, a noticeable sensitivity to the input parameters in the discrete element models is noted; therefore, characterization of material properties is necessary for reliable predictions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Research integrity | 0.000 | 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".