The importance of using silica fume and pumice powder in cement-based fiber composites, with a focus on microstructural and mechanical assessments
Bibliographic record
Abstract
This study aims to evaluate the combined effect of pumice powder and silica fume, used as a binary supplementary cementitious material (SCM) blend, on the microstructural, mechanical, and pull-out properties of steel fiber-reinforced cementitious composites. Concrete cylinders and prisms were prepared with varying steel fiber contents (0.5–1.5 %) and binary SCMs (10 % silica fume combined with 10 % or 20 % pumice powder). Experimental tests were conducted to determine compressive strength, tensile strength, flexural strength, modulus of elasticity, Poisson’s ratio, and bond behavior through steel rebar pull-out tests. Microstructural analyses included FTIR, XRD, TG/DTG, and field emission scanning electron microscopy (FESEM). The results demonstrated that the optimal mixture, containing 20 % pumice powder and 10 % silica fume, significantly enhanced the compressive strength by up to 120 %, the modulus of elasticity by 17 %, the flexural strength by up to 68 %, and the pull-out resistance by up to 73 % compared to the control sample. Additionally, this blend improved the pore-filling effect, promoted the consumption of portlandite, and facilitated the formation of C-S-H/C-A-S-H phases, thereby confirming the positive effect of pumice powder and silica fume on the performance of steel fiber-reinforced cementitious composites.
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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) | 0.001 | 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".