Comparative Performance of SF, PPF and Hybrid Fiber-Reinforced Self-Compacting Lightweight Concrete under Fire Exposure
Bibliographic record
Abstract
This study endeavors to interrogate the fire resistance properties of self-compacting lightweight concrete reinforced with Steel Fibers (SF), Polypropylene Fibers (PPF), and a hybrid combination of the two.Exposure to fire flame can significantly compromise the mechanical properties of concrete, leading to deterioration and spalling, which in turn, jeopardizes the structural integrity and performance of the material.In this investigation, concrete specimens, cured for 28 days, were subjected to fire temperatures of 300℃, 450℃, and 600℃, in line with ISO-834 practical curve.The heating environment was controlled by twenty-seven burners strategically positioned to ensure uniform heating.The thermal gradients across the specimens' cross-sections were monitored through thermocouples embedded inside at various locations.The experimental variables considered were the type and volume dosage of fiber reinforcements.Steel fibers were dosed at 0.25 and 0.5 volumes, while polypropylene fibers were introduced at 0.15 volume.Hybrid combinations of SF and PPF were also examined.For comparative purposes, a reference mix devoid of fiber reinforcements was prepared.The study revealed that at 600℃, the incorporation of steel fibers alone contributed to enhanced residual strengths-compressive, splitting tensile-and ultrasonic pulse velocity, outperforming both the hybrid fiber combination and the polypropylene fibers.It was observed that PPF began to melt, initiating a volume reduction at 160℃, and leading to increased porosity and microcrack development as the temperature approached 600℃.The best resistance to spalling was evidenced in the lightweight concrete reinforced with the hybrid fibers.This research provides crucial insights into the fire resistance of self-compacting lightweight concrete and the role of fiber reinforcement in enhancing structural resilience under high-temperature conditions.These findings have significant implications for the design and construction of fireresistant structures.
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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".