Numerical Study on Stress Concentration Factors at Splice Welds of Structural Steel Pipes
Bibliographic record
Abstract
Structural steel tubular elements are widely used in offshore structures, such as for marine piles, risers, and jacket bracings. Transversal weld is a common form for the splice of the structural steel pipes. The stress concentration at the splice weld is a critical effect to be considered in the structural analysis and design of the steel tubular elements. Researchers have provided solutions to the stress concentration factors (SCFs) for the splice welds under axial loading. Using finite element software ANSYS, this paper investigated the stress concentration due to in-plane bending moment. Various geometric configurations, such as diameter and thickness, determined according to normal marine steel tubular piles were modeled in the numerical models, and typical V-shape welding forms were adopted. Particularly, different thickness transitions applied in practice were modeled. Hot-spot stresses were determined based on the two-point linear extrapolation recommended by DNV-RP-C203. The stress concentrations at both toe and root were investigated. In addition, approximate solutions to the SCFs were derived based on flat-plate assumption. The computational results showed that the flat-plate solutions agreed with numerical solution in a reasonable manner, and correction factors were accordingly developed for the approximate solutions. It was also found that pipe thickness and thickness transition demonstrated significant impacts on the SCFs, while pipe diameter seemed less important. The numerical study establishes a fundamental database for the SCFs at pipe splice given in-plane bending moment. The findings will significantly support the practical fatigue assessment of spliced marine structural steel pipes subjected to complex loading effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".