Influence of shear strengthening of reinforced normal concrete beams incorporating sustainable materials
Bibliographic record
Abstract
Abstract This research studies shear strengthening performance for Normal Concrete (NC) beams employing Engineered Cementitious Composite (ECC) reinforced with Galvanized Welded Wire Mesh (GWWM) in conjunction with/without a prestressing system. Six full‐scale Reinforced Concrete (RC) beams were experimented under static monotonic loading at critical shear span zone up to collapse. The investigated factors were: the type and technique of strengthening, materials used for strengthening, and size and configuration of shear reinforcement. Two techniques were introduced: ECC layer reinforced with GWWM and pretensioned steel bars recovered by ECC with one GWWM. GWWM with and without pretensioned steel bars were delivered as shear reinforcement for strengthening. Vertical and inclined steel bars were the two configurations selected for the prestressing technique for suppressing shear stress. The study was discussed through the experimented beams' crack load and pattern, collapse mode, elastic stiffness, absorbed energy, initial cracking load, and ultimate load, along with the corresponding defection. Experimental results showed that both exploited techniques could govern crack patterns, enhance the failure mode, and upgrade ultimate loading capacity up to 52%. Finite element models were built up, simulating those strengthened with an ECC covering layer augmented with GWWM extracting a model with an error of about 3%.
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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".