Bond Performance of GFRP Bars Embedded into Low Carbon Concrete and Portland Cement Blocks
Bibliographic record
Abstract
Retrofitting of strength-deficient reinforced concrete (RC) infrastructure assets, in particular bridges, is an issue of global significance.In the context of sustainable development and climate change mitigation, demand for alternative low carbon building materials that can reduce the construction sector's large carbon footprint is increasing.The integration of fibre-reinforced polymer (FRP) materials to RC structures has become a prevalent sustainable alternative due to their high strength-to-weight ratio and durability, corrosion resistance and enhancement of shear capacity.The deep embedment (DE) technique is a superior technique for concrete shear strengthening.In this method, vertical holes are drilled upwards from the soffit in the shear spans of existing concrete beams.High viscosity epoxy resin is then injected into the drilled holes and steel or FRP bars are embedded into the concrete core.This study investigates, for the first time, the bond performance of deep embedment (DE) glass fibre reinforced polymer (GFRP) bars epoxy-bonded into a new type of low carbon, alkali-activated concrete (AAC) which is tested against Ordinary Portland cement (OPC).The experimentally investigated parameters include the concrete type, concrete compressive strength, GFRP bar diameter and hole diameter.The results show that the pull-out capacity increased with the increase in bar diameter and concrete compressive strength.The increase in hole diameter affected both the initial stiffness and failure loads of the specimens with DE GFRP bars.The results suggest that adopting a hole diameter of 1.5db enhances the bond behaviour of the DE technique.The specimens with low carbon, AAC achieved higher pullout capacities and better bond performance than the corresponding specimens with OPC, making AAC an eco-friendly alternative to enhancing the sustainability of large-scale construction without compromising the efficiency of the DE technique.
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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 | 0.001 |
| 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) | 0.002 | 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".