Design equations for maximum stress concentration factors for concrete-filled steel tubular K-joints
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it