Insights into the influence of hemp size and proportion on the strength and thermal performance of hempcrete
Bibliographic record
Abstract
Hempcrete is a sustainable bio-composite known for its excellent thermal insulation. This study explores the effects of hemp hurd size and content on the mechanical and thermal performance of hempcrete to determine the optimal mix parameters. Four hemp hurd types were examined: HS1 (coarse, long flaky hurds from Canadian harvest), HS2 (medium flaky hurds from Canadian harvest), HS3 (fine hurds from Canadian harvest) and HS4 (well-graded short hurds from a French harvest). Five hemp contents (20, 40, 50, 67 and 100 wt.% of binder) were tested. HS4 hurds (3–10 mm) provided the best grading, reducing porosity and enhancing strength, achieving compressive strengths of 1.33 MPa (dry) and 0.77 MPa (wet). Coarser hurds (HS1, HS2) increased porosity, while finer hurds (HS3) raised binder demand and interfacial failure risk. Hempcrete with HS4 exhibited the highest thermal conductivity (0.131 W/(m∙K)), as fine particles hindered heat diffusion while coarse ones increased void content. Increasing hemp content decreased density, strength, conductivity and heat capacity, with thermal diffusivity governed by the balance between conductivity and capacity. Thermal modelling indicated energy losses of 27% at 24 h for 20 wt.% hemp and 14% for 100 wt.%. Overall, well-graded hurds (HS4) with optimised hemp content achieved the optimal strength–insulation balance.
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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 | 0.001 |
| 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".