Investigating the fracture toughness of weld in S355 KT-40 offshore jacket leg using scanning electron microscopy and nanoscale modelling
Bibliographic record
Abstract
Welds are critical in cyclically loaded offshore jackets. This structure is utilized in wind energy farms and oil and gas processing. The study evaluates welds by welding experiments utilizing S355 KT-40 72 mm thick welded by flux-cored arc welding with gas-shielding (FCAW-GS). The investigation focuses on the grain-coarsened heat-affected zone (GCHAZ) of the weld within the jacket’s leg, utilizing optical microscopy (OM), scanning electron microscopy (SEM) combined with electron backscatter diffraction (EBSD), and nanoscale simulations and modelling through both analytical and numerical methods. OM and SEM techniques provide data regarding microstructural phases. EBSD yields information regarding phase fractions, crystal structures, and lattice characteristics. Alpha (α)-iron body-centered cubic, constitutes 93% of the primary phase, whereas gamma (γ)-iron face-centered cubic, accounts for 0.16%. Analytical and numerical modelling utilize the second derivative of energy with respect to volume (d2E/dV2) through quadratic equations, exponential functions, and finite difference techniques, which are essential for determining the bulk modulus. The effective fracture toughness of the weld in the GCHAZ region is determined based on stress intensity factors, resulting in values of 117 and 118 MPa.√m, respectively. The modelling of fracture toughness presented in this study proved beneficial as a supplementary tool for physical fracture toughness testing.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".