Reliability Analysis of Shear Tab Connections under Progressive Collapse Scenario
Bibliographic record
Abstract
"Progressive collapse" occurs when a localised failure of a structural component triggers propagation of failure to adjoining elements, eventually resulting in collapse of the entire structure or a major part of it. This phenomenon in steel structures is mitigated primarily through beam-to-column connections. In particular, simple shear connections are critical as they are the most common beam-to-column connections in steel structures and experience different load combinations than those considered in conventional design. They experience extreme rotation and axial tension due to catenary action developed in the frame after the removal of an adjoining column. With increasing experimental evidence and detailed numerical models, prediction of the behaviour and failure of shear connections is increasingly feasible. However, uncertainties in such predictions are unavoidable due to the uncertainties in the material and geometric properties. This paper aims to investigate the reliability of the connection capacity predictions due to variability in the connection parameters. As such, high-fidelity, finite-element-based models - including the connection components (plate, bolts, beams) - have been analysed to examine the performance and behaviour of the shear connections under different geometric variables. Models include a single 6-m-span beam with shear tab connections at both ends and application of a concentrated load at the removed column. Uncertainties in the plate dimensions - such as end distance, plate thickness, and hole diameter - have been considered for the reliability and sensitivity assessments. Explicit quasi-static analysis using ABAQUS is employed to overcome the convergence difficulties related to contact, geometric and material nonlinearities, large deformation, and fracture simulation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".