Gaussian Process Regression–Based Model Error Diagnosis and Quantification Using Experimental Data of Prestressed Concrete Beams in Shear
Why this work is in the frame
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Bibliographic record
Abstract
To facilitate considering model uncertainty for rigorous reliability/probabilistic analysis, this paper proposed a Gaussian process regression–based (GPR-based) model error quantification framework and applied to shear capacity prediction of prestressed concrete (PC) beams. Firstly, the model error of shear capacity models from five well-received concrete structure and bridge design codes were diagnosed based on a compiled experimental database, where systematic correlations between model error and model parameters were observed. To consider the systematic correlation, model error was then calibrated as a function of model parameters based on GPR. Different covariance functions were considered, and a model selection was conducted based on 10-fold cross validations. Then, the model error quantification performance was evaluated by investigating the residual systematic correlation between model error and model parameters, as well as by comparisons with the traditional professional factor approach. In the end, relative importance of model parameters on the model error for each design code were analyzed, indicating that the shear span-to-effective depth ratio is the most important source of model error for all considered design code models.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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