Toward green precast concrete production with BFRPmf: latest insights on the effect of fibre dosage on fresh and hardened properties
Bibliographic record
Abstract
This paper presents recent insights gained from some activities at QUB within the scope of the "Dispersed Non-Metallic Reinforcement for Energy-Efficient Manufacturing of Precast Concrete" project. These activities focused on developing and optimising fibre-reinforced concrete (FRC) mix designs through variations in basalt fibre-reinforced polymer macro fibre (BFRPmf) dosage. FRC mixes were developed by varying BFRPmf dosages (0.25%, 0.5%, 0.75%, and 1% fibre volume in the mix (Vf)) in a self-compacting concrete (SCC) matrix, and their fresh and hardened properties were measured. Rheological tests including the slump flow test, V-funnel test, and J-ring test were conducted to assess fresh properties. Compressive and tensile strength tests were performed, using the tensile splitting test and direct tensile test (DTT), to evaluate the mechanical properties of hardened concrete. The observations and measurements presented in this paper show that the addition of BFRPmf to the mixes slightly improves the compressive strength of the reinforced concrete in comparison to the reference plain SCC (No Fibre mix). The tensile strength of BFRPmf-reinforced concrete (BmfRC) mixes increases with the increase of macrofibre in the mix. Moreover, a higher fibre dosage improves the post-cracking behaviour and toughness of the BmfRCs, enabling them to carry the load for a longer duration after cracking. The study of rheological and mechanical properties highlights that despite achieving a high tensile strength improvement by adding macrofibre dosages of more than 0.5% Vf to the mixes, the passability and viscosity of fresh mixes are significantly compromised. Therefore, based on this study a fibre dosage of 0.5% Vf is identified as the threshold for optimal mix design.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".