Bibliographic record
Abstract
Joint-free bridges are preferred over those with conventional expansion joints because they provide better protection for structural components and allow such assets to reach their intended service life. Strategies for implementing joint-free bridges include the incorporation of link slabs (LS) and sliding approach slabs (SAS). Since LS and SAS need to be flexible to function well, the use of conventional steel-reinforced concrete is not ideal. This study explores the use of high-performance materials such as engineered cementitious composite (ECC) and glass-fibre reinforced polymer (GFRP) rebars for joint-free bridges under static or fatigue loading through experimental, numerical, parametric studies, and design-oriented analysis. It was found that high-performance materials helped to improve the performance of joint-free bridges with link slabs by providing flexibility, crack control, and fatigue resilience. The improved compatibility between ECC and GFRP contributed to better LS structural performance, as both materials contributed by sharing stresses under static and fatigue loading compared to steel rebar. Reinforced concrete deck girder joint-free bridges incorporating ECC link slabs were also investigated under static and fatigue loading, and showed structural performance comparable to that of composite deck steel girder bridges. It was also determined that ECC SAS performed well under static and fatigue loading, as well as in the case of approach fill soil settlement.
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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 | 0.000 |
| 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 | 0.000 | 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".