Eco-Efficient Fiber-Reinforced Concrete: From Mix Design to Fresh and Hardened State Behavior
Bibliographic record
Abstract
Fiber-reinforced concrete (FRC) mixtures often face challenges in the fresh state, which are typically addressed using high Portland cement (PC) content or chemical admixtures, obstructing sustainability efforts in the construction industry. Therefore, this study employs advanced mixed design techniques, specifically particle packing models (PPMs), to proportion eco-efficient FRC mixtures with reduced cement content (<300 kg/m3) while achieving desirable fresh and hardened state properties. Twelve low-cement (LC) FRC mixtures, containing limestone filler (LF) as an inert material and a partial replacement for PC, were designed with a water-to-cement ratio of 0.64, incorporating two fiber types (polypropylene and steel) at varying contents (0.5% and 1.0% by volume) and lengths (38 mm and 50 mm). PPM-designed mixtures used two coefficients of distribution (q-factors: 0.21 and 0.26) and were evaluated for fresh (VeBe time, slump, and rheology) and hardened (compressive strength and flexural performance) state properties. Results show that PPM-designed FRC mixtures achieved up to 70% higher compressive strength and up to 64% greater flexural capacity compared to conventional mixes (i.e., American Concrete Institute—ACI), despite using 20% less cement. Additionally, PPM mixtures exhibited higher VeBe times (up to 24 s) and yield stress, reflecting improved packing density, while demonstrating shear-thinning behavior for practical applications (i.e., pumped or vibrated concrete). Finally, the findings demonstrate that PPMs enable the development of eco-efficient, low-cement FRC mixtures with similar or improved hardened state performance and reduced environmental impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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 teacher head, 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".