Approximate Method for Performance-Based Seismic Assessment of Steel Moment-Resisting Frames
Bibliographic record
Abstract
bstract A wide range of approximate methods has been historically proposed for performance-based assessment of frame buildings in the aftermath of an earthquake. Most of these methods typically require a detailed analytical model representation of the respective building in order to assess its seismic vulnerability and post-earthquake functionality. This paper proposes an approximate method for estimating story-based engineering demand parameters (EDPs) such as peak story drift ratios, peak floor absolute accelerations, and residual story drift ratios in steel frame buildings with steel moment-resisting frames (MRFs). The proposed method is based on concepts from structural health monitoring, which does not require the use of detailed analytical models for structural and non-structural damage diagnosis. The proposed method is able to compute story-based EDPs in steel frame buildings with MRFs with reasonable accuracy. Such EDPs can facilitate damage assessment/control as well as building-specific seismic loss assessment. The proposed method is utilized to assess the extent of structural damage in an instrumented steel frame building that experienced the 1994 Northridge earthquake.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".