Flexural and Compressive Strength of Sustainable Concrete with Electric Arc Furnace Slag Aggregates and Polypropylene Fibers
Bibliographic record
Abstract
The objective of this study is to evaluate the effect of polypropylene fibers on flexural strength and tensile strength of concrete in which natural aggregates were replaced with 30%, or 50% recycled Electric Arc Furnace Slag (EAFS) coarse aggregates.The partial replacement of natural aggregates with EAFS aggregates is intended to preserve natural aggregates while recycling slag, a byproduct of industry, therefore, achieving a more sustainable concrete.Since EAFS aggregates may be used for slabs or slabs-on-grade, in addition to other structural elements, mechanical properties in general and flexural strength are essential properties.The fundamental goal of using polypropylene fibers, however, remains the control of cracking.The w/b ratio was maintained at 0.45 for all mixes regardless of EAFS replacement percentage or size of fibers.Two types of fibers were evaluated, Polyolefin-based microfibers ranging from 11 to 13 long with a diameter of 35±5 𝜇𝑚, and Polypropylene macrofibres that are 48 mm long and 0.48 mm in diameter.The flexural and splitting tensile strengths were determined for samples cured in water for 28 days while compressive strength was determined after 7, 28, and 56 days of curing.After 7 days of curing, samples with micro and macro fibers developed slightly higher compressive strength than the control mix without fibers.However, after 28 days and 56 days of curing, there was negligible effect for polypropylene fibers on compressive strength, regardless of the content of EAFS aggregates.Nonetheless, the flexural strength of concrete with polypropylene fibers, with and without ACBFS aggregates, was higher than the control mix without fibers.The increase of the 28-day flexural strength of concrete with polypropylene fibers compared to control mix was largely similar (14.1% to 15.2%), regardless of EAFS aggregates content.Macrofibres achieved slightly better 28-day flexural strength compared to control mix with natural aggregates.Similarly, polypropylene fibers increased the 28-day splitting tensile strength compared to control mix, but with a much smaller range of 3.1% to 5.1% compared to control mix.The 5.1% increase in splitting tensile strength was the mix with 50% EAFS aggregates.The study paves the road to using EAFS aggregates to produce environmentally friendly concrete with polypropylene fibers to control cracking and enhance flexural strength. .
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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".