Design equations for maximum stress concentration factors for concrete-filled steel tubular K-joints
Bibliographic record
Abstract
Stress concentration factors (SCFs) for welded tubular joints can be decreased by filling the chord with concrete leading to a longer fatigue life. However, there are currently no design formula available in guidelines to predict the SCF of concrete-filled circular hollow section (CFCHS) K-joints, thus limiting their applicability in bridge design. To address this gap, finite element models for CFCHS K-joints were developed and compared against test results to ensure their accuracy. Then, a comprehensive parametric study was conducted to establish relationships between maximum SCFs and four variables: brace-to-chord diameter ratio ( β ), chord diameter-to-thickness ratio (2 γ ), brace-to-chord thickness ratio ( τ ), and the angle between braces and chord ( θ ). A total of 480 FE models were examined under three loading conditions including brace and chord loading: balanced axial force, chord axial force, and chord bending. Design equations to predict the maximum SCF for CFCHS K-joints were established by multiple regression analyses of the numerical results. A comparison of maximum SCFs between circular hollow section (CHS) and CFCHS K-joints was made, and it was concluded that average reductions of 42% and 33% in maximum SCFs in CFCHS K-joints at the locations of the chord and brace were found compared to CHS joints for balanced axial force, respectively. Finally, a case study illustrating how to use the proposed equations for fatigue safety verification was presented. • Studying the concrete-filled steel tubular (CFST) K-joints. • No stress concentration factor (SCF) design equations available for CFST K-joints. • FE modelling, validation and parametric study conducted under three loading conditions. • Design equations proposed to estimate the maximum SCFs. • Filling the chord with concrete reduces SCF by 3%–42%.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".