Sensitivity analysis of the influence of ground motion intensity levels on the seismic behavior of steel frames in assessment of the target displacement considering near-fault effects
Bibliographic record
Abstract
Nonlinear time-history analysis conducted as part of a performance-based seismic design approach often require that the ground motion records are selected and then scaled to a specified level of seismic intensity. In such analyses, besides an adequate structural model, a set of acceleration time-series is needed as the most realistic representation of the seismic action. In this paper, the effects of scaling procedure on seismic demands of steel frames are investigated. To this, two special steel moment-resisting frames with considerable higher mode effects, and two sets of ground motions, including near-fault and far-fault motions are considered. Moreover, three scaling procedures are introduced for performing nonlinear dynamic time-history analysis of structures. Among different demands, lateral roof displacement and interstory drift are selected as seismic demands. The height-wise distribution of demands shows that the inelastic seismic demands of the near-fault pulse-like ground motions differ considerably from those of far-fault ones. These results show that the story drifts are mostly larger for far-fault motions in the upper story levels in comparison to near-fault records and in the lower floors, the reverse is true. Thus, the scaling procedures directly affect the results of seismic demands and choosing different methods would result in varying responses. Moreover, a low-cost and fairly effective procedure is proposed to estimate the target displacement demands of buildings from response-spectrum analyses, considering near-fault effects. The precision of this method is verified by nonlinear time-history analysis results, as the benchmark solution, and acceptable improvements have been achieved.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".