Generating Drift‐Free, Consistent, and Perfectly Spectrum‐Compatible Time Histories
Bibliographic record
Abstract
Abstract The focus of this article is on generating spectrum‐compatible acceleration, velocity, and displacement time histories for seismic analysis and design of engineering structures. If a generated acceleration time history is integrated to obtain the corresponding velocity and displacement time histories, it has been found that there are usually drifts in the resulting processes. Such drifts are due to overdeterminacy in the constants of integration. Baseline correction, although widely used, is not a suitable remedial measure to remove drift because it distorts the frequency content and renders the corrected processes no longer mutually consistent. The objective of this article is to develop an efficient and accurate method for generating drift‐free, consistent, and spectrum‐compatible time histories, which are essential properties for these time histories to be used as seismic input in time history analysis. To ensure drift‐free and consistent behavior, the eigenfunction method is applied to expand the time histories in eigenfunctions of a sixth‐order ordinary differential eigenvalue problem. The influence matrix method considering the influence of one frequency component on all others is capable of achieving perfect spectrum compatibility which has never been accomplished.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".