Performance‐Based Seismic Vulnerability Evaluation of Masonry Buildings Using Applied Element Method in a Nonlinear Dynamic‐Based Analytical Procedure
Bibliographic record
Abstract
A thorough four‐step performance‐based seismic evaluation for a six‐story unreinforced masonry building is conducted. Incremental dynamic analysis is carried out using the applied element method to take advantage of its ability to simulate progressive collapse of the masonry structure including out‐of‐plane failure of the walls. The distribution of the structural responses and inters‐tory drifts from the incremental dynamic analysis curves are used to develop both spectral‐based (Sa) and displacement‐based (interstory drift) fragility curves at three structural performance levels. The curves resulting from three‐dimensional (3‐D) analyses using unidirectional ground motions are combined using the weakest link theory to propose combined fragility curves. Finally, the mean annual frequencies of exceeding the three performance levels are calculated using the spectral acceleration values at four probability levels 2%, 5%, 10%, and 40% in 50 years. The method is shown to be useful for seismic vulnerability evaluations in regions where little observed damage data exists.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Seismic vulnerability analysis of a masonry building; structural engineering.
This evaluates seismic vulnerability of masonry buildings rather than research practice.
Civil/earthquake engineering analysis of masonry seismic vulnerability.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".