Experimental investigation of pre‐damaged circular <scp>RC</scp> columns strengthened with <scp>fabric‐reinforced</scp> cementitious matrix (<scp>FRCM</scp>)
Bibliographic record
Abstract
Abstract This paper aims at investigating the efficiency of strengthening pre‐damaged circular reinforced concrete (RC) columns with Polyparaphenylene Benzobisoxazole fabric‐reinforced cementitious matrix (PBO‐FRCM). The investigated parameters were the degree of pre‐damage (non‐damaged, damaged monotonically up to yielding, or fatigue damaged), the number of PBO‐FRCM layers (0, 2, or 4), and the tie spacing (180 or 90 mm). The experimental results of testing 12 short columns under axial compression showed that PBO‐FRCM systems enhanced the capacity of the non‐damaged columns and successfully restored and further enhanced the capacity of the pre‐damaged columns. Columns strengthened with two and four PBO‐FRCM layers showed a gain in the capacity of up to 40% and 75%, respectively. Furthermore, PBO‐FRCM wrapping enhanced the ductility of both the non‐damaged and the pre‐damaged columns, in which ductility improvement was more noticeable in columns with the larger tie spacing. Finally, the theoretical load‐carrying capacity calculated using ACI 549 provisions showed a good agreement with the experimental capacity of all tested columns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".