Structural Evaluation of Shiplap Hinge Joint Using Empirical and Strut-and-Tie Methods
Bibliographic record
Abstract
Bridges designed before 1990 with shiplap hinge joints (SHJs) using classical approaches need to be evaluated to verify minimum reinforcing or anchorage and development length requirements to failure mechanisms that may occur as outlined in the AASHTO LRFD Bridge Design Specifications (2020).In addition, limited studies to date have focused on the consequences of these older bridge designs and their associated failure mechanisms when evaluating beam ledges with SHJs using classical approaches.In this study, the behaviour of SHJs in existing bridges is examined analytically using two methods, empirical and strut-and-tie, to demonstrate the potential application of each technique on assess existing structures.Most importantly, this study provides insight on how strut-and-tie methods can be applied to evaluate existing bridges with in-span hinge connections and how to adequately account for development lengths using the strut-and-tie method compared to the empirical method.Nonlinear finite element (FE) models are generated as a physics-based to represent the expected ultimate capacity and associated failure mechanisms of beam ledges.The results revealed that the estimated strength capacity of the SHJs using the strut-and-tie method was less than both empirical and FE methods, suggesting that the lower-bound solution may be the more critical evaluation method.Overall, the results illustrate the various governing failure mechanisms from the different methods when evaluating the section capacity, sufficient steel area, and development length, which influence the structural response of SHJs when loaded.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".