Self-Healing Potential and Post-Cracking Tensile Behavior of Polypropylene Fiber-Reinforced Cementitious Composites
Bibliographic record
Abstract
The use of synthetic fibers as reinforcement in fiber-reinforced cementitious composites (FRCC) demonstrates a combination of better ductile response vis-à-vis metallic ones, enhanced durability in a high pH environment, and resistance to corrosion as well as self-healing capabilities. This study explores the effect of macro- and micro-scale polypropylene (PP) fibers on post-crack energy, ductility, and the self-healing potential of FRCC. Laboratory results indicate a significant change in fracture response, i.e., loss in ductility as curing time increases. PP fiber samples cured for 2 days demonstrated ductile fracture behavior, controllable crack growth during tensile testing, post-cracking behavior, and a regain in strength owing to FRCC’s self-healing mechanism. Different mixes of FRCC suggest an economical mixing methodology, where the strong bond between the PP fibers and cementitious matrix plays a key role in improving the tensile strength of the mortar. Additionally, the micro PP fiber samples demonstrate resistance to micro-crack propagation, observed as an increase in peak load value and shape deformation during compression and tensile tests. Notably, low volume fraction of macro-scale PP fibers in FRCC revealed higher post-crack energy than the higher dosage of micro-scale PP fibers. Lastly, few samples with a crack of < 0.5 mm exhibited a self-healing mechanism, and upon testing, the healed specimens illustrated higher strain values.
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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) | 0.000 | 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".