Investigation on the Calibration of Numerical Models for Cast Steel Replaceable Modular Yielding Links in Steel Eccentrically Braced Frames
Bibliographic record
Abstract
ABSTRACT A new generation of yielding links, referred to as cast steel replaceable modular yielding links (CMLs), was recently validated through extensive large‐scale testing, to enhance the seismic performance of steel eccentrically braced frames (EBFs). The effective use of CMLs relies on accurately evaluating the seismic response of the EBF systems, which requires robust calibration of the hysteretic model that simulates the nonlinear behavior of CMLs. Calibration relevance (CR) is a recently developed metric to evaluate the effectiveness of calibration methods for hysteretic models in structural seismic analysis. This study aims to use CR evaluation to examine various calibration methods for CMLs, to understand the impact of different aspects in calibration and to offer recommendations for robust CML model calibration. The CR evaluation is conducted on two prototype EBF buildings with two and four stories. Two modeling approaches of CML with different fidelities are considered for the reference and simulation cases in the CR framework. Four quantification methods for calibration error, which serve as objective functions in optimizing hysteretic model parameters, are investigated. Additionally, both standardized and more realistic loading histories (LHs) are considered. For the hysteretic model that is used, it is found that LHs featuring smaller peak link rotations, ranging from 0.03 to 0.07 radians, lead to more accurate calibration overall. The reasoning for this observation is the limitation of the hysteretic models, which is explained in detail at the end.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".