Engineering Properties of Gypseous Soils Improved with Natural and Industrial Fibers
Bibliographic record
Abstract
The significant challenges facing geotechnical engineers concerning gypseous soils and their behavior under water flow require careful assessment of gypsum soil performance under wet conditions.Improving gypsum soils through the inclusion of enhanced additives is among the most widely employed methods.The major objective of the current study was to investigate the effect of fiber additives on the engineering properties of gypseous soils experimentally.Natural fiber has been mixed into sandy and clay soils in several studies, but gypseous soils have not been investigated.However, the study investigated the properties of gypseous soils with three gypsum content (19%, 36%, 62%) improved by an agricultural waste of sugarcane bagasse (SCF) used as natural fiber and polypropylene (PPF) as an industrial fiber, these materials are economic, renewable and eco-friendly.The effect of fibers on compaction characteristics, specific gravity, and shear strength parameters at both dry and soaked conditions (soaked in water for 1 day) is investigated.Fibers used by percentage (0-0.8%by weight of dried soil).From the result of soil improved by polypropylene fibers (PPF), The significant increase was observed in cohesion under both dry and soaked conditions, surpassing the cohesion increment observed in soil treated with SCF. in dry conditions for soil treated by (PPF) the increment was recorded (20%-126%), and for soil treated by (SCF), the increment was recorded (19%-81%).But the angle of internal friction of the soil improved by SCF in dry and soaked conditions was higher than that soil treated by PPF, in dry condition for soil treated by PPF the increment was recorded (8%-33%) and (21%-54%) for soil treated by SCF.shear strength parameters in the dry condition are more than the increment in soaked condition for treated soil by (PPF and SCF), also from the results can be obtained the optimum fiber content was 0.6%, and 0.4% for SCF and PPF respectively.The max. dry unit weight and specific gravity for three types of soils decreased by increasing fiber content but optimum moisture content increased by increasing fiber content.Lastly can be concluded the PPF gave better results than SCF.
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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 | 0.000 |
| 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) | 0.001 | 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".