Assessment of Mechanical, Thermal Insulation and Water Sorption Properties of Natural Fiber-Cement Composites
Bibliographic record
Abstract
The growth of awareness about the environmental issues caused by the construction industry has fueled innovation in sustainable building practices in recent years. Of particular concern is the need for solutions to reduce the high carbon footprint of current building materials such as cement and concrete. A potential remedy to the adverse environmental effects of the construction industry is the replacement of non-renewable materials with a natural and easily renewable counterpart. This study investigates the effects of fiber type, fiber volume fraction, and fiber size on the compression, flexural, thermal insulation and water absorption properties of natural fiber cement composites. The fibers used were obtained from Alberta based crops: wheat straw and hemp hurd. These fibers were each sieved and characterized in two size categories, coarse and fine, and added to the cement at 3 volume fractions: 5%, 10%, and 15%. The results indicate that the addition of fibers to cement decrease the compression strength, increase the flexural strength, increase the thermal resistivity, and increase the saturation moisture content of the composite compared to the control (unreinforced cement). In addition, a moisture sorption model based on Fick’s law showed reasonable fit to the experimental data. Overall, this study demonstrated that cements reinforced with natural fibers offer improved flexural (crack resistance) and insulating properties compared to unreinforced cement which may be advantageous in a number of building product applications (e.g. architectural or nonstructural components). However, these natural fiber composites also had reduced compressive strengths, and were more susceptible to moisture uptake. These characteristics may affect the usage of these materials in main structural components, and may also require proper protection from moisture in outdoor applications.
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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".