Triaxial drained behaviour of disposable face mask fibre reinforced sand
Bibliographic record
Abstract
The widespread usage of disposable face masks (DFM) during the COVID-19 pandemic has exacerbated waste management challenges, prompting an investigation into their potential reuse as a soil reinforcement material. Previous researchers have investigated the effect of mask fibres on pavement subbases and the environmental problems caused by these fibres. This study examines the mechanical properties of sandy soil enhanced with shredded and layered DFM under triaxial testing conditions, focusing on key parameters like shear resistance, elastic modulus, stress-strain characteristics, axial resistance, failure envelope, and brittleness index. Results show that adding DFM significantly improves soil cohesion, friction angle, shear strength, and peak deviatoric stress, especially at higher fibre contents and relative densities. However, increased DFM fibre content was associated with reduced elastic modulus, which stabilised in specimens with layered DFM, suggesting complex interactions between DFM content and soil mechanics. Concerns include potential void formation leading to asymmetric settlement and environmental issues on non-biodegradable fibre integration in soil. These findings highlight the need for meticulous mixture preparation, large-scale studies, and environmental assessments to evaluate the impact of using DFM in soil reinforcement, particularly for road construction and slope stabilisation. This research provides crucial insights into the potential of DFM for soil reinforcement.
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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) | 0.000 | 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) | 0.002 | 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".