Surrogate-based calibration and prediction of hysteretic model parameters for steel beam-column bolted T-stub connections
Bibliographic record
Abstract
The nonlinear behavior of steel beam-column bolted T-stub (BCBT) connections under cyclic loading can potentially be represented using the Pinching4 model in OpenSees . However, its application is limited by the lack of parameter specifications tailored to these connections, particularly for parameters controlling pinching effects and stiffness degradation, which are not directly observable from experimental data. Manual calibration of these parameters is challenging due to the model's implicit nature and high-dimensional parameter space. To address these challenges, this study (1) proposes a novel calibration approach by combining Polynomial Chaos Expansion (PCE) and Genetic Algorithm (GA) to calibrate the Pinching4 model parameters for tested specimens available in the literature, and (2) develops predictive models for specifying parameters for future use of the Pinching4 model for steel BCBT connections. Specifically, PCE is utilized to approximate the discrepancy between the Pinching4 model predictions and experimental data to reduce computational cost, while GA is employed for its robust global search capabilities in optimization tasks. Furthermore, Elastic Net (ENet) regression is used to develop predictive models for the Pinching4 model parameters for untested connections, effectively handling high-dimensional features and small sample sizes, thereby mitigating multicollinearity and overfitting issues. The resulting regression models demonstrate favorable predictive performance, enhancing the accessibility and practical application of the Pinching4 model in modeling the moment-rotation hysteretic behavior of untested steel BCBT connections. • Proposed a novel calibration approach by combining PCE and GA. • Calibrated hysteretic model for steel BCBT connections. • Developed predictive models for hysteretic parameters using ENet regression. • Proved the potential of Pinching4 model for steel BCBT connections.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".