A comprehensive assessment of sustainable mortar containing tannery sludge and wheat straw fibre
Bibliographic record
Abstract
The shortage of raw materials for construction and the problems related to waste management make the use of industrial and agricultural by-products appealing, while also offering advantages in terms of lower weight and cost. Consequently, the development of environmentally friendly mortars based on tannery sludge and wheat straw fibres (WSF) can improve thermal insulation and sound absorption while maintaining adequate mechanical strength. In this context, different mortar samples were produced with an optimal sludge content and different fibre proportions (1 to 6%) in the cement binder mass. The mortars were characterised by microstructural, mechanical, thermophysical and acoustic analyses. The incorporation of low WSF content into the mud mortar significantly improved the thermomechanical behaviour of the composite. The density and thermal conductivity of the mortars measured after 28 days decreased with the fibre content increased. Adding 6 wt.% WSF reduces the dry composite’s lighter by 15% and enhances its thermal insulation capacity by about 70%. Furthermore, the samples with high straw content show an improved acoustic absorption in 500–1000 Hz audio range. The 4 wt.% WSF-cemented composite fulfils the structural specifications of lightweight concrete, where the crack-bridging mechanism contributes to mortar reinforcement by enhancing tensile and flexural responses, suppressing crack initiation and propagation, and improving fracture toughness, thereby ensuring reliable mechanical integrity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".