A Clustering‐Based Loading History Selection Method for the Calibration of Buckling‐Restrained Braces in Seismic Analysis
Bibliographic record
Abstract
ABSTRACT The accuracy of engineering demand parameters obtained from nonlinear time‐history analysis (NTHA) is crucial in a performance‐based earthquake engineering framework. Hysteretic models are commonly used for predicting the nonlinear response of critical structural components and are essential for ensuring the accuracy of NTHA results. Hysteretic models are typically calibrated based on the experimental data from a quasi‐static test utilizing a standardized reversed‐cyclic loading protocol. Recent studies, however, have shown that this conventional model calibration method may lead to inaccurate dynamic response of a structural system because the standardized reversed‐cyclic loading history (LH) is unrealistic compared to what the component would experience in a structural system subjected to earthquake ground motions. These studies have demonstrated the benefits of using more realistic LHs for hysteretic model calibration by evaluating the calibration relevance (CR) of different calibration methods. The objective of this study is to extend the framework of evaluating calibration methods and to provide additional insights and recommendations to enhance the robustness of model calibrations. This is achieved by analyses conducted on a suite of buckling‐restrained braced frames (BRBFs). First, a comprehensive global sensitivity analysis (GSA) of parameters for a commonly used hysteretic model is conducted based on a probabilistic input model that was derived previously from multiple hybrid simulations. The GSA is conducted by evaluating Sobol’ indices using a metamodel‐based approach with polynomial chaos expansions (PCEs). Next, 20 features are extracted from each realistic LH considering the characteristics in the transitional and plastic ranges of the corresponding hysteresis curve. A clustering‐based LH selection criterion based on these features is then proposed to identify an optimal cluster of LHs exhibiting greater CR values, which are desirable in achieving higher accuracy in the global model of the structural system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".