Tension and shear block failure of bolted gusset plates
Bibliographic record
Abstract
Despite the large database of test results for tension and shear block failure in gusset plates, the exact progression of the failure mechanism is not clear. Although current design equations predict the capacity of gusset plates fairly well, it is important for a design equation to not only predict the capacity reliably but also reflect the failure mode accurately. Recent experimental and numerical research has indicated that current design equations do not always predict the failure behaviour accurately. A finite element model was therefore developed to predict the sequence of events that leads to the tear-out of a block of material from a bolted gusset plate in tension. The model was developed to provide a useful tool for studying tension and shear block failure in gusset plates and other structural elements. This paper presents the development of the finite element model and procedure for prediction of tension and shear block failure in gusset plates. Making use of the finite element model, the database of test results is also expanded to include gusset plates with a larger number of transverse lines of bolts than what has been obtained experimentally. A reliability analysis is used to assess several design equations, including the equation adopted in CAN/CSA-S16-01 and a unified equation proposed recently for several types of bolted connections. From this work, a limit states design equation is proposed for gusset plates.Key words: gusset plate, limit states design, reliability, shear rupture, tension rupture, finite element analysis, failure criterion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".