Digitally Augmented Database of Fracture-Critical Steel Beam-to-Column Connection Tests
Bibliographic record
Abstract
This paper describes a recently compiled database of 100 full-scale steel beam-to-column connections that failed due to flange fractures. This database focuses on welded flange connections tested in the past 50 years, including tests with strong panel zones and box columns that have been excluded from previous collections. This database is augmented with high-fidelity structural models carefully calibrated to the test data using a semiautomatic algorithm to extend the information from each experiment beyond the recorded response. Once calibrated, these models offer a versatile method to decompose the total displacement response of the connections in beam, panel zone, and column deformations and extract more detailed response quantities, such as the stress history of the flange. This augmented database enables a deeper understanding of the causes of flange fractures and an assessment of the common rotation limits in ASCE/SEI 41 employed for simulating fracture. The results show that these rotation limits have a considerably large error. Furthermore, these rotation limits are incapable of either identifying the flange that would fracture first or simulating the opening and closing behavior of a fractured flange. The stress histories of the flange extracted using the models is a more efficient demand parameter for characterizing fracture behavior. This database is openly available in the DesignSafe DataDepot and is immediately useful for researchers developing new models for beam-to-column connections susceptible to fracture and to practicing engineers interested in calibrating structural models for nonlinear dynamic analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".