Symmetric and Asymmetric Strengthening of Two-Span RC Beams Using FRCM Systems
Bibliographic record
Abstract
This paper reports on the feasibility of using fabric-reinforced cementitious matrix (FRCM) systems to strengthen two-span reinforced concrete (RC) beams that are structurally deficient in their sagging regions. In addition to one unstrengthened control beam, nine beams strengthened either symmetrically or asymmetrically with polyparaphenylene benzobisoxazole (PBOFRCM), carbon (CFRCM), and carbon fiber–reinforced polymer (CFRP) sheets were tested under a five-point load configuration. Test results showed that increasing the strengthening ratio resulted in significant increases in the yielding and load-carrying capacity of the beams. Beams symmetrically strengthened with PBOFRCM showed high ductility indices ranging between 100% and 121% of that of the control beam, whereas those strengthened with CFRCM and CFRP showed ductility indices of 45% and 34% of that of the control beam, respectively. Moreover, beams symmetrically strengthened with PBOFRCM systems encountered moment redistribution ratios between 42% and 82% of that of the control beam compared with 10% and 9% only for those strengthened with CFRCM and CFRP systems, respectively. The asymmetric strengthening configuration in which FRCM systems were used along with CFRP sheets proved to be an efficient method to enhance the ductility and moment redistribution capacity of the strengthened beams. Analytically, the rigid-body-rotation approach was modified to predict the moments and curvatures at the plastic hinges of the strengthened sections. The predicted moments and curvatures showed a notable agreement with the experimental values.
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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 | 0.000 | 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".