Nonlinear modelling parameters and acceptance criteria in ASCE/SEI 41: a critical review and applicability to Canada
Bibliographic record
Abstract
The National Research Council Canada is currently developing seismic evaluation and upgrading guidelines (SEG) for existing buildings in Canada. The SEG consist of both linear and nonlinear analysis procedures to evaluate the seismic adequacy of existing buildings. Where nonlinear analysis procedures are selected, nonlinear modelling parameters (NMP) and acceptance criteria (AC) are to be used. Due to the lack of Canadian guidelines for nonlinear analyses, the state of the practice in Canada often refers to ASCE/SEI 41 for guidance. Given the differences in seismic design and construction practices in the United States and Canada, ASCE/SEI 41 should be used with caution. This technical note presents a critical review of NMP and AC in ASCE/SEI 41 and recommends key steps for the investigation of the applicability of NMP and AC in ASCE/SEI 41 to the Canadian context. An example is included to demonstrate the recommended steps and the importance of such investigation.
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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 | 0.043 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".