An Engineering Comment for Simply Accelerating Seismic Response History Analysis of Mid-Rise Steel-Structure Buildings
Bibliographic record
Abstract
Response history analysis using a time integration method is a powerful versatile tool in accessing structures seismic behaviours. To reduce the analysis run-time, a technique was proposed in 2008 for time integration with steps larger than the steps of ground motions. The technique has been implemented in seismic assessment of frames, buildings, bridges, silos, etc., leading to considerable reductions in the analysis run-time, without notable effect on the response accuracy. The technique has recently been named as the SEB THAAT (Step- Enlargement-Based Time-History-Analysis-Acceleration-Technique). To use the SEB THAAT, the smallest dominant period of the response needs to be available prior to the analysis. In this paper, concentrating on 5-20-floor steel-structure buildings, a simple engineering comment is proposed that eliminates this need. As a result, in response history analysis of mid-rise steel-structure buildings subjected to ground motion, by using the proposed comment, we may reduce the analysis run-time, significantly, without any initial information about the response. The reduction is 50% for the linear analyses.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".