The role of fiber surface treatment on improving mechanical performance of cementitious composites
Bibliographic record
Abstract
Polypropylene (PP) fibers are widely used in fiber-reinforced composites due to their mechanical benefits; however, their non-polar nature results in poor adhesion with the cementitious matrix, resulting in premature debonding failure and limiting overall composite performance. This research proposed a novel adhesive-based surface treatment approach using siliceous materials as a supplementary cementitious material and investigates the impact of this surface modification on the mechanical and microstructural properties of fiber-reinforced cementitious mortar (FRCM) and fiber-reinforced concrete (FRC). PP fibers at three volume fractions—0.3 %, 0.6 %, and 0.9 %—were incorporated into mortar and concrete mixes to assess the effect of the treatment. Mechanical tests, including compression, tensile, flexural, and pullout tests, revealed significant improvements in the treated fiber samples, with the pullout tests showing the most pronounced enhancement in bond strength. Notably, surface-treated fibers demonstrated improved energy absorption in both pre-crack and post-crack phases, highlighting their effectiveness as crack retarders and in crack-bridging mechanisms. In addition, contact angle measurements show improvement in adhesion capabilities of surface-treated fibers. Microscopic analysis utilizing scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX/EDS) demonstrated that the surface treatment significantly enhanced the mechanical properties of the matrix while also improving the interfacial transition zone (ITZ) between the fibers and the surrounding matrix. Despite the challenges of achieving uniformity in the manual coating process, the study underscores the potential of silica-based fiber surface treatment to significantly improve the structural performance of FRC and FRCM by enhancing bond strength and energy dissipation capacities. • A novel adhesive-based SCM Surface treatment significantly improves the bond between PP fibers and the matrix. • Incorporating surface-treated PP fibers into the matrix effectively enhances both pre-crack and post-crack resistance. • SEM and EDX analyses revealed that the applied surface treatment improves the interfacial transition zone (ITZ). • At the current stage, the proposed manual coating method is labor-intensive without an automated coating setup.
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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) | 0.001 | 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".