Seismic evaluation of concrete moment frames in existing government buildings
Bibliographic record
Abstract
Many government departments have hundreds of buildings located in active seismic regions. Most of these buildings were built decades ago according to old design codes, and could be vulnerable to strong or even moderate earthquakes. To evaluate the seismic performance of concrete moment frames or moment frames with shear walls in these buildings, the static, vibration and modal response spectrum analyses are carried out according to the 2005 National building code of Canda NBCC. The analysis uses 2-D finite element models consisting of frame elements and inplane elements. The frame element has a built-in rigid linear segment at each end for modeling the portion within the beam–column joint, whereas the inplane element may have openings for modeling doors and windows in shear walls. The stiffness of both elements is adjusted to include effects of shear deformation in beams and bending deformation in wall piers. The results are further adjusted to incorporate effects of torsion and accidental torsion. Then, CSA-A23.3-04 is followed for detailed evaluation on safety in limit states, “strong column – weak beam” concept and shear strength requirements. Based on this approach, a new computer program is developed to perform this evaluation with minimum input data. Important issues in each step are discussed in detail. Examples are presented, and results are compared with available existing data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".