Extension of a Material Model for Pipeline Steels
Bibliographic record
Abstract
Pipeline steel stress-strain curves obtained from tension and compression testing of longitudinally and circumferentially oriented specimens of the pipe wall can be significantly different e.g., the pipe material is anisotropic. The anisotropic behavior can result from the manufacturing process (e.g., due to cold expansion of UOE pipe) and can also be influenced by strain aging effects (e.g., due to heated application of pipe coating materials). As described in previous work, the Mroz multilinear kinematic hardening plasticity theory has the ability to accurately model different types of anisotropic pipe material behavior including relatively “sharp” uniaxial circumferential tension response and relatively well-rounded uniaxial longitudinal tension and compression response. The stress-strain curve fitting is accomplished by essentially selecting the sizes and initial positions of elliptical von Mises yield functions in stress-space. A previously developed and published 8-parameter model is well-suited for fitting a matched pair of longitudinal tension (LT) and hoop tension (HT) stress-strain curves as might typically be available from a strain-based pipeline design project. Fitting a pair of “target” LT-HT stress-strain curves is accomplished using a “2-root” fitting procedure where the roots correspond to locations where the yield functions intercept the stress axes in two-dimensional (longitudinal-hoop) stress space. In this paper, the previously described 8-parameter/2-root fitting procedure is extended to a 10-parameter/3-root fitting procedure for situations where a matched “triple” of pipe steel stress-strain curves are available (e.g., LT, HT and longitudinal compression or LC). This extension allows for analysis of strain-based design conditions using an analytical pipe steel, which provides an accurate representation of the uniaxial longitudinal and circumferential stress-strain response of the pipeline material. This paper reviews the 8-parameter/2-root fitting procedure and outlines the extension to the 10-parameter/3-root fitting approach including example application.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".