Compression Splices of GFRP Bars in Unconfined and Confined Concrete Columns
Bibliographic record
Abstract
Although the use of glass fiber–reinforced polymer (GFRP) bars as compression reinforcement in RC columns has been extensively investigated, no research has been conducted on spliced GFRP bars under compression. This study presents an experimental program aimed at providing more insight into the effects of confinement on the behavior of spliced GFRP bars under compression. The test variables were confinement and compression-bar splice length. The test matrix included 14 circular concrete columns that were tested under concentric compression loading. Nine of the specimens were confined with GFRP spirals at two different spacings (40 and 80 mm), whereas the rest were reinforced longitudinally without transverse reinforcement. The test results are presented and discussed in terms of load–displacement behavior, failure mode, splice strength, and load–strain behavior. The postpeak behavior of the columns improved significantly with increasing levels of confinement. The bond and end-bearing contributions are described in detail. A regression analysis of the experimental results is presented, along with an analytical model proposed for predicting the strength of spliced GFRP bars under compression. The model estimates splice strength with satisfactory accuracy.
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 | 0.001 |
| 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 | 0.001 |
| 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".