Application of Gaussian Process Regression for the Accuracy Assessment of a Three-Dimensional Strain-Based Model
Bibliographic record
Abstract
Abstract Dents are one of the common integrity threats of long-distance transmission pipelines. The current CSA Z662 standard assesses dents based on the dent depth. However, the severity of dent features is a function of many factors. Most recently, numerical modeling via finite element analysis (FEA) has been utilized to assess dent severity, however the approach is computationally expensive. Recently, the authors’ research group developed a robust but much simplified analytical model to evaluate the strains in dented pipes based on the geometry of the deformed pipe. When the strain distribution predicted using the analytical model is benchmarked against the strains by nonlinear FEA they showed a good agreement with certain error. The procedure, however, predicts more conservative results in terms of the maximum equivalent plastic strain (PEEQ). In order to estimate the accuracy in the recently developed model, a series of nonlinear FEA pipe indentation simulations were conducted using the finite element analysis tool, ABAQUS and compared with the analytical prediction. This paper presents an application of a Bayesian machine learning method named Gaussian Process Regression (GPR) for the accuracy assessment of the developed analytical model for dent strain assessment, quantifying the error in comparison with the FEA in terms of the maximum PEEQ. The Gaussian Process (GP) model holds many advantages such as easy coding, prediction with probability interpretation, and self-adaptive acquisition of hyper-parameters. By varying the dent depth and the indenter radius, this paper provides a model that quantifies the error in the developed analytical model. The proposed model can be utilized to rapidly determine the severity of a dent along with the accuracy of the prediction. This analysis method can also serve as a reference for other analytical expressions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".