Cost and Ductility Effectiveness of Concrete Columns Strengthened with CFRP and SFRP Sheets
Bibliographic record
Abstract
Recently, steel fibre reinforced polymers (SFRP) sheets have been introduced for the repair and rehabilitation of concrete structures. Few researchers studied the behaviour of the concrete columns wrapped with SFRP sheets; however, several critical parameters such as the cost and ductility effectiveness of the SFRP wrapped concrete columns have been lightly addressed. Thus, the main objective of this paper is to study the cost and ductility effectiveness of SFRP wrapped concrete columns and compare the results with the conventionally used carbon FRP (CFRP) wrapped concrete columns. In addition, an analytical procedure to predict the cost effectiveness of SFRP wrapped concrete columns is also suggested, from which, a parametric study was conducted. The parametric study investigated the effect of the concrete strength, the number of SFRP layers, and the size and slenderness effects on the cost effectiveness of the concrete columns wrapped with SFRP sheets. The results from the cost and ductility effectiveness study indicated that the SFRP wrapped concrete columns showed enhanced performance over the CFRP wrapped concrete columns. The suggested analytical procedure proved to be a reliable and accurate method to predict the cost effectiveness parameter of SFRP wrapped concrete columns. The parametric study showed the significant impact of the investigated parameters on the cost effectiveness of concrete columns wrapped with SFRP sheets.
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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) | 0.000 | 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 | 0.000 | 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".