Assessment of Shear Connection through Composite Beam Modeling
Bibliographic record
Abstract
Steel-concrete composite construction is used extensively in bridges across North America. The welded shear stud is the standard connector used today, but other connectors, such as the through-bolt connector, may have advantages for precast construction or applications where better fatigue resistance or deconstructability is desired. The standard method of assessing the performance of a shear connector is through the use of push tests. However, the load-slip curves that result from these tests do not accurately predict load-slip behavior at the shear interface of the beams and girders they are meant to simulate. In this paper, a model is presented that predicts composite beam behavior using elastic material properties and nonlinear shear connector load-slip curves. The finite element (FE) model features link connector elements between a steel beam and concrete slab that can be programmed to simulate different connector types. Although the model can be used with push test load-slip curves as inputs, it is shown that a much better prediction can be made using force-deformation data from experimental beam tests or FE analysis. Results are discussed for stud connectors and through-bolt connectors, and it is shown that while through-bolts allow more interfacial slip and overall deflection, material stresses and composite interaction are not affected as much as might be expected. The outcome of this work is a comparison tool which can be used to assess the viability of current and future shear connection alternatives with the goal of achieving an economical and structurally sound shear connector.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".