Cementitious Composites with Cellulose Nanomaterials and Basalt Fiber Pellets: Experimental and Statistical Modeling
Bibliographic record
Abstract
The production of high-performance fiber-reinforced cementitious composites (HPFRCCs) as a durable construction material using different types of fibers and nanomaterials critically relies on the synergic effects of the two materials as well as the cementitious composite mixes. In this study, novel HPFRCCs were developed, which comprised high content (50%) slag by mass of the base binder as well as nano-silica (NS) or nano-crystalline cellulose (NCC). In addition, nano-fibrillated cellulose (NFC), and basalt fiber pellets (BFP), representing nano-/micro- and macro-fibers, respectively, were incorporated into the composites. The response surface method was used in this study’s statistical modeling part to evaluate the impact of key factors (NS, NCC, NFC, BFP) on the performance of 15 mixtures. The composites were assessed in terms of setting times, early- and late-age compressive strength, flexural performance, and resistance to freezing-thawing cycles, and the bulk trends were corroborated by fluid absorption, thermogravimetry, and microscopy tests. Incorporating NS/NCC in the slag-based binders catalyzed the reactivity of cement and slag with time, thus maintaining the setting times within an acceptable range (maximum 9 h), achieving high early- (above 33 MPa at 3 days) and later-age (above 70 MPa at 28 days) strength, and resistance to fluid absorption (less than 2.5%) and frost action (DF above 90%) of the composites. In addition, all nano-modified composites with multi-scale fibers showed notable improvement in terms of post-cracking flexural performance (Residual Strength Index above 40%), which qualify them for multiple infrastructure applications (i.e., shear key bridge joints) requiring a balance between high-strength properties, ductility, and durability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".