Numerical modeling of rolling loading of bridge deck with different reinforcement types
Bibliographic record
Abstract
Abstract This paper presents a complex nonlinear finite element model of a full‐scale bridge deck that was experimentally studied under both pulsating and rolling load. The bridge deck was designed to incorporate three different reinforcement designs implemented at various portions of the slab to maximize test efficiency. The different stiffness across the longitudinal span poses a challenge for the finite element model to simulate the behavior of the bridge deck. The deck is comprised of zones incorporating different types of reinforcements, namely conventional steel rebar, glass fiber‐reinforced polymer (GFRP) rebar, and a new GFRP stay‐in‐place structural form system. All zones are integrated in one monolithic cast. The finite element model features characteristics such as nonlinear material laws, failure criteria, and interfacial relations for parts in contact. To verify the applicability and adequacy of selected material models, 14 specimens representing three different concrete structures and test setups from independent studies were modeled and validated. A model of the bridge deck was then created and validated using the data from the experiment featuring service load level.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".