Impact of Ferro-Geopolymer Encapsulation on the Structural Integrity of Concrete Columns Under Axial Load
Bibliographic record
Abstract
This research explores the impact of ferro-geopolymer encapsulation on the axial loadbearing capacity and structural integrity of concrete columns.Ferro-geopolymer, a composite material that merges ferrocement and geopolymer technology, offers potential advantages in enhancing the mechanical properties of concrete.Experimental tests were performed on columns encapsulated with ferro-geopolymer jackets, subjected to axial compression and also studied the slump cone test on fresh state concrete, compressive strength and sorptivity test were conducted on hardened concrete.Results indicated significant improvements in compressive strength compared to unreinforced columns.The encapsulation provided effective confinement, delaying cracking and increasing ultimate load capacity.Slump cone test showed higher value with 100% of fly ash addition.F50G50 mix showed higher compressive strength values at 7, 28 and 90 days of 29.77MPa, 45.72MPa and 46.19MPa, respectively.G100 mix showed lower sorptivity values compared to all mixes.Three layeres of expanded and wire mesh showed better load carrying capacity, higher deflection ductility index and more energy ductility index values under axial loading.The findings suggest that ferro-geopolymer encapsulation is a promising technique for retrofitting and strengthening concrete columns, offering a sustainable and efficient solution for improving structural resilience.
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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) | 0.000 | 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) | 0.001 | 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".