Development of Seismic Vulnerability Curves for Masonry Buildings Using the Applied Element Method
Bibliographic record
Abstract
As an approach to the problem of the seismic vulnerability evaluation of existing buildings using the predicted vulnerability method, analytical procedures are applied to produce vulnerability curves for different building classes. For some building types, mainly masonry structures, the development of those curves will be complicated and time consuming if a Finite Element-based method is used. Therefore, the Applied Element Method is used here to develop fragility curves for those challenging building classes. The incremental dynamic analysis of a 6-storey industrial masonry building built in 1906 in Montreal, Canada has been carried out using 14 sets of synthetic and real ground motions representing three M, R categories. Intensity and damage measures are pointed on the IDA curves at three structural performance levels, immediate occupancy, life safety, and collapse prevention, for each ground motion. The statistical analysis of those points is then carried out to develop fragility curves for the masonry building at each performance level. To show the effect of the building typology, those fragility curves are compared with the fragility curve provided by NIBS in HAZUS-MH MR1 Technical and User's Manual for masonry buildings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".