Surface Morphology, Bond Strength and Failure Behaviour of Steel vs Basalt Rebars in Alkali Activated Concrete
Bibliographic record
Abstract
This microscopic characteristics of basalt bars after exposure to alkaline solutions and pullout performance of steel and basalt rebars embedded in conventional and alkali-activated concrete were studied. The overarching goal is to assess the potential of basalt rebars as an alternative to conventional steel reinforcement in alkali activated concrete elements. To understand their durability in alkaline environments, especially within alkali-activated concrete (AAC), steel and basalt rebars were subjected to an accelerated exposure test in high-pH simulated concrete pore solutions, for 24hrs. This aimed to replicate the aggressive internal environment typically found in AAC and mixes with supplementary cementitious materials. Post-exposure microscopic inspection revealed morphological changes, but the changes were limited to the adhesives and did not influence the basalt fibre bundles. Pullout tests were conducted in reference to BS EN 1881:2006, using a 50:50 blend of CEM I and GGBS. AAC mixes used 6% and 8% alkali dosages. Both steel and basalt rebars exceeded the 75kN standard threshold. Steel rebars reached up to 115kN, while basalt rebars recorded 100–120kN. Predominant failure mode was midsection concrete splitting. Slightly higher bond strength in basalt-reinforced AAC samples may result from interface roughening caused by the initial high alkaline exposure(i.e., to activator solution). These findings highlight the viability of basalt rebars as a low-carbon, structurally reliable alternative to steel in AAC systems.
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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".