Performance of seawater sea sand coral aggregate concrete columns reinforced with hybrid glass fiber reinforced polymer and epoxy‐coated steel bars under axial compression
Bibliographic record
Abstract
Abstract To improve the ductility of fiber reinforced polymer reinforced concrete structures while addressing the corrosion of steel bars and promoting the utilization of local materials, seawater sea sand coral aggregate concrete (SSCC) columns reinforced with hybrid glass fiber reinforced polymer (GFRP) bars and epoxy‐coated steel bars (ECSB) provide a viable solution for marine structures and island engineering. This innovative solution combines GFRP bars and ECSB to ensure axial load carrying capacity while improving ductility. In addition, considering local materials, ecological benefits, and good durability, it provides the possibility for large‐scale construction of marine engineering. This study considered three research variables, namely strength grades (C30, C40, and C50), reinforcement ratios (1.01%, 1.56%, and 2.26%), and reinforcement types (GFRP bars [G group], ECSB [E group], and GFRP bars and ECSB [GE group]). A total of 36 concrete columns were studied to evaluate their mechanical properties, including axial load carrying capacity and ductility. Regarding axial load carrying capacity, with the same reinforcement ratios and strength grades, the SSCC columns reinforced with ECSB exhibit the highest axial load carrying capacity, followed by SSCC columns reinforced with hybrid GFRP bars and ECSB, while SSCC columns reinforced with GFRP bars (G‐SSCC) show the lowest. Regarding ductility, the hybrid reinforcement type significantly improves the ductility of the column compared to G‐SSCC columns. Furthermore, the axial load carrying capacity and ductility of columns were predicted using American Concrete Institute 440.1R‐15, Canadian Standards Association S806‐12 (R2021), and the finite element software ABAQUS. The analysis results show good agreement with the experimental results.
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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 | 0.001 | 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".