Uncertainty quantification in the calibration of numerical elements in nonlinear seismic analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Modeling uncertainty in structural models can greatly affect the reliability of nonlinear time history results, which are central to performance‐based earthquake engineering. A crucial source of modeling uncertainty is the uncertainty in the parameters of constitutive models, which simulate the hysteretic behavior of key structural components. In current research and engineering practice, it is assumed that the accuracy of a nonlinear structural model is achieved by component calibration, which is conducted by trying to best match the response of a numerical model of a component to test results under a standardized quasi‐static loading regime. However, previous research has shown that even a very well‐fitted component‐level calibration might result in considerable errors in the system‐level structural dynamic response. This study is an initial attempt to investigate calibration relevance incorporating a rigorous uncertainty quantification framework. In the proposed framework, parameters of a constitutive model are considered as random inputs. Calibration error at the component level and global error at the system level are quantified based on the discrepancies between the simulation models with probabilistic inputs and reference models. Polynomial chaos expansions (PCEs) metamodels are implemented to conduct sensitivity analysis and investigate calibration relevance. Three buckling restrained braced frames (BRBFs) with different heights are investigated using the proposed framework. Four calibration methods’ relevance with global errors based on three engineering demand parameters (EDPs) are studied. The results allow for the identification of optimum hyperparameters to achieve peak calibration relevance and to evaluate different calibration methods for several EDPs for the three BRBFs.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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