Hybrid‐simulation‐based model calibration method for nonlinear seismic analysis
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The calibration of parameters of hysteretic models that simulate the hysteretic behavior of key structural components is a crucial task in the nonlinear seismic analysis of structures to ensure accurate analysis results. For complicated systems, a direct calibration of model parameters at the system level is almost impossible due to the lack of test data. Consequently, the calibration is usually conducted using test results with lower levels of complexity. Currently, a widely accepted practice in calibrating hysteretic model parameters in structural models is to utilize standardized cyclic tests of a single component. However, due to the simplified and unrealistic loading profile of standardized cyclic tests, the relevance between the calibration and the system‐level prediction capabilities can be weak. In other words, a well‐tuned hysteretic model that matches the standardized cyclic test results very well may not be able to produce the same level of accuracy in estimating the system‐level structural dynamic response where the calibrated components will experience more random and complicated loadings. In this paper, a method is proposed to calibrate hysteretic models in a test method with more realistic loading histories through hybrid simulations. The proposed calibration method is then validated by conducting a large number of hybrid simulations on a type of small‐scale buckling‐restrained brace (BRB) specimen. A framework is also proposed to evaluate the relevance between the calibration and the system‐level response considering uncertainties in hysteretic model parameters. The results demonstrate the superiority of the hybrid‐simulation‐based calibration method over the conventional cyclic‐test‐based calibration method.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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