Evaluating In-Span Hinge Connections: Empirical Methods and Strut-and-Tie Analysis
Bibliographic record
Abstract
Shiplap hinge joints (SHJs) can alter the intended load paths and affect the structural performance of various bridge components, including the deck and piers.This issue is especially significant for older bridges with SHJs designed using traditional methods, which may not meet the minimum reinforcing, anchorage, and development length requirements specified in the AASHTO LRFD Bridge Design Specifications.The detailed finite element (FE) models are employed to examine load paths, mechanical contributions, and effective stress along rebar.It aims to compare the ultimate capacity and associated failure mechanisms of beam ledges with SHJs as predicted by both empirical and strut-and-tie methods.The analyses, conducted according to current AASHTO LRFD standards, illustrate the consequences of older bridge designs and their associated failure mechanisms when assessing beam ledges with SHJs.Additionally, the study offers insights into applying strutand-tie methods for evaluating existing bridges with in-span hinge connections and properly accounting for development lengths using the strut-and-tie method compared to the empirical method.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".