Analytical Model for Calculating Shear Capacity of NSC Beams Strengthened by UHPC Lateral Layers
Bibliographic record
Abstract
The utilization of ultrahigh-performance concrete (UHPC) as lateral or U-shaped thin layers to strengthen the shear-deficient normal-strength concrete (NSC) beams is an effective and promising way to increase their stiffness and shear capacity. However, the lack of an accurate analytical model to estimate the shear capacity of UHPC-strengthened beams could be a critical obstacle to its further application. This paper presents a new analytical model for calculating the shear capacity of UHPC-lateral-strengthened beams, which is the first to be based on the simplified modified compression field theory (SMCFT). The model is also distinguished by considering the full compressive and tensile stress–strain curves of UHPC instead of only using their ultimate strengths. By assuming a complete strain transfer at the NSC–UHPC interface and the coincidence of the principal stress and strain axes in the NSC substrate and UHPC layers, the strain compatibility between the two components are included in the shear calculation. The model predictions were validated in two steps, first by using principal strain measurements in the shear span of five beams from digital image correlation (DIC) data to confirm the strain evaluation of the model, and second by reproducing the shear capacities of 12 beams from five experimental campaigns. Results showed a clear agreement between the strains calculated by the analytical model and those measured in experiments. The shear capacities of UHPC-lateral-strengthened beams calculated by the analytical model obtained a very high accuracy and a low variance to replicate experimental results. The model allows evaluation of the contributions of each beam components (NSC substrate, stirrups, and UHPC layers). Results confirmed that the increase in the shear capacity of strengthened beams mainly comes from the high tensile strength of the UHPC and the thickness of the UHPC layer, as well as the improved contribution of the stirrups.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".