Effects of Domestic Sewage Sludge on Gypseous Soil's Geotechnical Properties
Bibliographic record
Abstract
Sewage sludge is a product of wastewater treatment plants.This study aims to evaluate adding different percentages of sewage sludge as a weight ratio to gypsum soil to develop some physical and mechanical soil properties.Samples of soil were obtained from the city of Ramadi, and the sewage sludge was accumulated directly at the Al-Rustamiyah wastewater treatment plant located north of Baghdad, Iraq.The laboratory experiment was undertaken on the original gypsum soil samples and the soil treated with sewage sludge ash.This study also involves testing XRF and energy-dispersive X-ray spectroscopy (ESD) of the sewage sludge material.There was a rise in the liquid limit and plasticity index for Atterberg limits.The compaction results indicated increased maximum dry density and optimal moisture content.The optimum unconfined compressive strength (UCS) value is obtained by adding 20% of sewage sludge.The best maximum dry density was achieved with 20% sewage sludge ash.After adding sewage sludge ash, the dry density of gypsum soil increased significantly to 1.65 g/cm 3 .Also, the optimum water content is gradually increasing.The bearing capacity ratio for the untreated soil was 5%, and the California Bearing Ratio (CBR) values were recorded as 22.3% for the gypsum soil treated with SSA at 20% with a curing time of 14 days.There is a significant increase in cohesion values with between 10 and 20 percent of the sewage sludge added to the soil.This increase has a significant effect on engineering soil behaviour.The elements are found in sewage sludge ash, based on XRF, EDS, and SEM tests: Si (10.5%),Al (3.6%), Ca (15%), and Fe (6.2%).Sewage sludge can be used successfully as an additive to improve the properties of gypseous soils by a weight ratio of 20%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".