Experimental Investigation of the Shear Resistance Mechanism on Hybrid NSC-UHPC Predamaged and Undamaged Unidirectional Bridge Slabs
Bibliographic record
Abstract
This paper investigates the shear behavior of unidirectional hybrid slabs made of a normal-strength concrete (NSC) substrate and an ultrahigh-performance concrete (UHPC) overlay on the tensile side. The impacts of two important aspects on the shear behavior were studied. First, various strengthening configurations (thickness, with or without NSC substitution, with existing and/or new rebars) were investigated, specifically two configurations scarcely studied in the scientific literature. Second, the state of damage of the NSC slab prior to the overlay application was examined for the first time. The hybrid slabs tested behaved monolithically until the establishment of a composite mechanical action occurring at up to 1.66 times the reference shear resistance. The composite mechanical action offered structural hardening with significant increase of shear resistance up to 2.50 times the reference shear resistance. Strain distribution and reorganization was monitored on the slabs using digital image correlation technology, and showed creation of a strut and tie system in four successive steps. Both the UHPC thickness and the total area of longitudinal rebar had a significant impact on the ultimate shear resistance. The effect of the load history on the ultimate shear resistance was limited, and partly masked by the combined effect of the type of NSC-UHPC interface and the overlay configuration influencing the crack pattern.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".