Prediction of Nonlinear Response—Pushover Analysis versus Simplified Nonlinear Response History Analysis
Why this work is in the frame
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Bibliographic record
Abstract
This paper discusses the pros and cons of predicting structural behavior and important engineering demand parameters (EDPs) by means of either nonlinear static pushover (NSP) analysis or nonlinear response history analysis (NRHA) with simple hysteretic models. It will be demonstrated that NRHA comes out as a clear winner if the issue is quantification of EDPs, except for low-rise first mode controlled structures in which torsion is not an important consideration. It also will be demonstrated that NSP analysis has much value in understanding important behavior characteristics that are not being explored in a NRHA in which engineers usually focus on a demand/capacity assessment rather than visualization of response. The conclusion is that both NSP and NRHA have intrinsic value and that it is advisable to employ a combination of both to understand seismic performance and quantify important engineering demand parameters.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it