Model of the Mechanical Behavior of Cementitious Matrices Reinforced with Nanomaterials
Bibliographic record
Abstract
CNTs and CNFs have been introduced as a nanoscale reinforcing material to cementitious composites, for stiffening and strengthening the microstructure. This technology is motivated by the need to control crack initiation in the cementitious gel before it propagates into visible crack formations. Experimental evidence supports this concept; however, testing at the nanoscale may only be conducted through nanoindentation, which has a limited range only providing localized results that cannot be extrapolated to general stress states. To evaluate the restraining action of nanomaterials in the gel microstructure, a computational mechanistic model has been developed where the material phases (gel, nanotubes, and pores) are modeled explicitly allowing for natural randomness in their distribution and orientation. Repeated analysis with identical input data reproduces the statistical scatter observed in laboratory tests on identical material samples. The formulation uses a discrete element approach; the gel structure is represented by a random network of hydrates and successfully reproduces the known trends in mechanical behavior of cementitious materials (pressure and restraint sensitive material behavior) and the small ratio of tensile to compressive strength. Simulations illustrate that it is possible to computationally reproduce the measured properties and behavior of fiber-reinforced cement composites using information from simple laboratory tests.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".