Seismic Demand Estimation of Inelastic SDOF Systems for Earthquakes in Japan
Bibliographic record
Abstract
An accurate estimation of the maximum inelastic displacement of a structure under seismic excitations is essential to quantitative seismic risk assessment. The seismic performance of existing structures can be evaluated by utilizing inelastic single-degree-of-freedom (SDOF) systems and carrying out nonlinear dynamic analysis. This article develops a probabilistic model of the peak ductility demand of inelastic SDOF systems with various hysteretic characteristics using comprehensive sets of strong ground-motion records observed in Japan. The use of a large database facilitates the systematic investigation of the effects of earthquake type, record selection criteria, seismological parameters, and seismic region on the inelastic seismic demand. Nonlinear dynamic analysis of inelastic SDOF systems is carried out for statistical analysis and probabilistic modeling of the peak inelastic seismic demand. Analysis results indicate that the inelastic seismic demand depends on earthquake type, selection criteria, and seismological parameters to some degree. The most notable differences in inelastic seismic demands are observed for interface records at short vibration periods in comparison with crustal and inslab records; the differences can be explained by different response spectral shapes of the datasets. The inelastic seismic demand for the California crustal records is greater than that for the Japanese crustal records at short vibration periods, whereas the demands are comparable at long vibration periods. The peak ductility demand can be modeled as a Frechet variate, and empirical equations for calculating its statistics are developed, which achieve simplicity and sufficiency in probabilistic seismic risk analysis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".