Reinforced cementitous composite with<i>In situ</i>shrinking microfibers
Bibliographic record
Abstract
Abstract This paper describes an innovative fiber reinforcement technology for cementitious composite structures that employs in situ shrinking microfibers to provide supplemental strength-enhancing compressive stresses. Reinforced concrete is one of the most commonly used structural materials in construction industry, primarily due to its cost, durability, ability to be easily fabricated into a variety of shapes on site, and locally abundant raw material availability almost everywhere. Unlike incumbent passive reinforcing microfiber technology, in situ shrinking microfibers that respond to an in situ stimulus such as heat, pH, or moisture variations can induce pre-compression to matrix and create additional resistance from external loads, creating stronger composite structures. In this paper, heat-activated-shrinking (HAS) microfibers made from polyolefin, and pH-activated-shrinking (pHAS) microfibers made from chitosan powder were used to study effects of shrinking microfiber reinforcing in concrete. Shrinking ratios and tensile strengths of both microfibers were measured. Cementitious specimens with active shrinking microfibers, passive non shrinking fibers, as well as control samples were made. Mechanical properties of the samples were compared with compression and three-point bending tests. The optimum microfiber weight percentages for HAS microfibers were 0.5 wt% in compression tests, and 1.0 wt% in three-point bending tests. For pHAS microfibers, the optimum weight percentages were 0.5 wt% in three-point bending tests. Compared to heat passive microfibers specimens, 45% increase in the maximum compression strengths, and 124% increase in the maximum bending strengths were achieved at the optimum weight percentages of HAS microfibers. In addition, with 0.5 wt% of pHAS microfibers, 145% increase in the maximum bending strengths of three-point bending tests resulted compared to pH passive microfibers specimens.
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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) | 0.001 | 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".