A Comprehensive Parametric Finite Element Study on the Development of Strain Concentration in Concrete Coated Offshore Pipelines
Bibliographic record
Abstract
The strain concentration at the field joint (FJ) of the commonly used concrete coated offshore pipelines is considered and discussed in this paper. The details of a 3D finite element (FE) modeling framework, developed using the commercial software ABAQUS, are presented. The numerical results are verified against the experimental results available in the literature. The FE model considered in this study captures several nonlinear phenomena associated with the problem, including the plastic deformation of the steel and anticorrosion layer (ACL) material, the cracking and crushing of the concrete, and also the large deformation effects. The developed FE framework is subsequently used to perform a parametric study to assess the effect of each influencing parameter on the strain concentration factor (SCF) developed within the FJ region. The influence of the geometric features of the coated pipe and the relevant mechanical properties of the materials as well as various combined loading scenarios are investigated. Results indicate that pipeline diameter, thickness, and coating thickness affect the SCF more than the strength of either concrete coating or ACL. Also, the postyield properties of the steel, especially the strain hardening capacity, may significantly influence the SCF. The combination of the internal pressure loading (causing a biaxial stress state) or tensile loading with the primary bending load is found to also increase the SCF significantly after steel yielding is initiated. Moreover, these combined loading scenarios cause different and more severe plastic deformation patterns in the FJ.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".