Bond performance of tensile lap-spliced basalt-FRP reinforcement in high-strength concrete beams
Bibliographic record
Abstract
This paper investigates the bond between high-strength concrete (HSC) and tensile lap-spliced basalt fiber-reinforced polymer (BFRP) bars. Ten large-scale BFRP-reinforced concrete beams (300 × 450 × 3900 mm) were fabricated and tested under four-point loading until failure. The parameters investigated included the BFRP bar diameter (10, 12, and 16 mm), the splice length (400–1200 mm range), and the bar surface texture (sand-coated (SC) and helically wrapped (HW)). Test results demonstrated that the flexural capacity of the beams reinforced with SC-BFRP bars was almost similar to that of beams reinforced with HW-BFRP bars. However, SC-BFRP bars showed a slightly higher bond with concrete compared to that of helically wrapped counterparts. The bond strength of spliced BFRP bars was inversely related to the splice length. Also, BFRP bars with larger diameter bars require longer splice lengths to reach their maximum capacity. Finally, the experimentally estimated critical splice lengths were compared to those calculated by existing models and code-based equations. Both ACI 440.1R-15 and CSA S806-12 provisions were conservative in predicting splice length for BFRP bars. However, the CSA-S6-14 design code was more accurate in estimating the splice length for BFRP with bigger diameters. Though, it was not conservative with smaller diameters.
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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 | 0.001 | 0.001 |
| 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".