Prediction of Moment–Curvature Response and Maximum Bending Resistance for Hybrid NSC-UHPC Elements
Bibliographic record
Abstract
Exceptional mechanical properties of ultra-high performance concretes (UHPC) offer strong strengthening capacities in bending and shear when used as overlay on normal strength concrete (NSC) structures. Nonetheless, lack of simple and intuitive design models for hybrid elements in design guidelines refrain designers from using UHPC overlays for structural applications. Thereby, a simplified sectional analysis model for NSC-UHPC hybrid elements was developed based on the philosophy of the Canadian Bridge Design Code CSA-S6. By using a new average stress distribution for NSC in hybrid elements that considers the strain at the extreme compressed fiber, equilibrium of forces can be solved by a second-degree equation with direct computation. The simplified model provides the complete moment–curvature behavior of hybrid elements for design purposes, thus allowing verifications in service and ultimate state conditions. An empiric equation is also proposed to evaluate the maximum bending capacity of hybrid elements for predesign. It only uses an approximation of a lever arm between forces in the hybrid cross section and thus offers a quick and easy way to evaluate the bending capacity. Both tools were validated on a detailed and iterative sectional analysis program and with results of four international experimental campaigns. The simplified sectional analysis model and empirical equation showed very good accuracy at reproducing the behavior of a wide range of NSC-UHPC hybrid elements configurations.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".