Utilizing realistic loading histories for the calibration of nonlinear components in seismic analysis
Bibliographic record
Abstract
Hysteretic models that simulate the hysteretic response of key structural components are generally employed in the nonlinear seismic analysis of structures. The calibration of hysteretic model parameters is crucial for achieving accurate analysis results in structural seismic assessment. The calibration process is commonly conducted by tuning hysteretic model parameters to align with the experimental results of a single component tested under standardized reverse-cyclic loading protocols. The underlying assumption of such a calibration method is that a structural model at the system level, using a well-tuned hysteretic model capable of accurately replicating the test results of a single component under a standardized incremental cyclic loading protocol, can predict the dynamic response of the structural system subjected to ground motion excitations with an acceptable level of accuracy. However, due to the simplified and often unrealistic loading protocols used for model calibrations, this assumption has been challenged recently by both numerical and experimental studies. In this paper, calibration methods utilizing more realistic loading histories are evaluated and compared to more conventional incremental cyclic loading-based protocols. The evaluation of calibration methods is carried out by quantifying the calibration relevance, utilizing a framework of virtual experiments that incorporates uncertainties in hysteretic model parameters. Analyses are conducted based on a case study of BRB components and BRBFs. Additionally, four calibration error quantification methods, considering characteristics in the transitional and plastic ranges of hysteresis curves of BRB, are proposed and compared. The results demonstrate that it is in fact advantageous to use realistic loading histories in component calibration of BRBs. An improved formulation of the calibration error is also proposed for the optimization of hysteretic parameters. • Validated the effectiveness of calibration methods for hysteretic models utilizing more realistic loading histories. • Proposed a novel method to quantify calibration errors focusing on the transitional and plastic ranges of hysteresis curves. • Analyzed BRBFs considering uncertainties in hysteretic model parameters to evaluate various calibration methods.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".