A performance‐based seismic loading protocol: The generated sequential ground motion
Bibliographic record
Abstract
Abstract A realistic performance‐based seismic loading protocol called generated sequential ground motion (GSGM) has been developed in this paper. GSGM is a ground motion fabricated from segments of real recorded ground motions that could enable the introduction of performance‐based seismic assessment and design to experimental testing in setups such as shaking table testing. It can also significantly reduce the number of nonlinear time history analyses required in performance‐based seismic design. The protocol optimizes the behavioral information output of an experimental test or numerical analysis by incorporating dynamic demands corresponding to design limit states with different probabilities of exceedance (i.e. 10%, 5%, and 2% in 50 years) in a single record. In addition, since the segments are matched to relevant target spectra, the number of ground motions required to estimate the mean response is reduced. This paper presents the algorithm developed to produce the GSGM. The capability of the GSGM to replicate the structural responses produced by code‐compliant suites, and a suite of 100 ground motions as a more robust estimation of the actual response is investigated. The results of the case study bridge pier show that the drift variation of the GSGMs compared to code‐compliant suites is within 10%. Compared to the estimate of the actual response, the drift variation of GSGMs and the code‐compliant suites is 20% and 15%, respectively, and the damage variation is 30% and 15%, respectively. Furthermore, considering other relevant intensity measures when producing GSGMs can reduce these variations. This study suggests that the GSGM can replicate structural responses of the current code procedures.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".