Sludge as an Alternative to Cement for Canal Lining
Bibliographic record
Abstract
Plain concrete is used for water canal lining due to its low permeability to reduce water losses due to seepage.However, cement manufacture has a negative environmental impact as it produces large amount of CO2 emissions in addition to high energy consumption.In this study, bio-sludge of sewage plants was used an alternative for cement, mixed with sand and crushed stone, and used as an alternative to plan concrete for canal lining.An experimental testing program was designed based on percentages of sludge and soil equal to 2.5%, 5%, and 10% by weight.For each sludge mix, properties were characterized such as particle size, density, and specific gravity.Also, shear strength properties were determined and California bearing ratio.The permeability of the sludge mix was also determined in laboratory.It was evident that mixing limited percentages of sludge with cohesionless soil significantly reduced the permeability.To assess the practicality of this approach for canal lining purposes, two trapezoidal in-situ trial pits were excavated in a sandy soil profile, one pit without lining and the other using sludge-mix lining.The seepage rate of water in each pit was monitored with respect to time after taking into consideration the water evaporation rate.Outcomes of the experimental program showed that sludge-soil mix can be used as an eco-friendly alternative in enhancing the properties of the canal lining soil.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".