Numerical Analysis and Design Provision Development for the Simple for Dead Load--Continuous for Live Load Steel Bridge System
Bibliographic record
Abstract
The proposed connections of steel bridge girders at the pier for the simple for dead load and continuous for live load concept (SDCL) were investigated. Analytical models were developed for four connection types that previously were tested in the structural lab. For the numerical modeling of the tested specimens, nonlinear finite element was utilized. The behavior of the numerical models of each tested specimen was verified by the experimental results. The force resistance mechanism of each specimen was studied using the experimental and numerical data. In developing a resistance mechanism for the connections, the yielding of the rebar in the slab, the steel girder bottom plate and the crushing of the concrete diaphragm at pier were considered. A set of detailed equations was developed to formularize the connection behavior at the ultimate capacity under negative flexure. The developed equations show a good agreement with the finite element analysis results. For practical purposes, simplified equations were derived from the more detailed equations to calculate the negative ultimate flexure capacity, at the section through the pier, for two connection types. To verify the proposed design equations for a wider range of bridges, a parametric study was performed. The nonlinear finite element models of the bridges from the parametric study were used for verification of the proposed equations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".