Modeling the Deformation Response of High Strength Steel Pipelines—Part II: Effects of Material Characterization on the Deformation Response of Pipes
Bibliographic record
Abstract
The material model proposed in Part I (Neupane et al., 2012, “Modeling the Deformation Response of High Strength Steel Pipelines—Part I: Material Characterization to Model the Plastic Anisotropy,” ASME J. Appl. Mech., 79, p. 051002) is used to study the deformation response of high strength steel. The response of pipes subjected to frost upheaval at a particular point is studied using an assembly of pipe elements, while buckling of pipes is examined using shell elements. The deformation response is obtained using two different material models. The two different material models used were the isotropic hardening material model and the combined kinematic hardening material model. Two sets of material stress-strain data were used for the isotropic hardening material model; data obtained from the longitudinal direction tests and data obtained from the circumferential direction tests. The combined kinematic hardening material model was calibrated to provide an accurate prediction of the stress-strain behavior in both the longitudinal direction and the circumferential direction. The deformation response of a pipe model using the three different material data sets was studied. The sensitivity of the response of pipelines to the choice of a material model and the material data set is studied for the frost upheaval and local buckling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".