Assessment of strength development of cemented desert soil
Bibliographic record
Abstract
Abstract For highway construction or any superstructure, on dune sand, designers and construction teams must ensure that the foundation soil is stable enough to provide support for the applied loads. Sand dunes are stretched across Libyan deserts that make road construction a challenge because of the poor soil base. Replacement of such a weak soil is very expensive and not economically feasible, and, in many cases, there is no alternative soil nearby. This study used two different mix designs aimed at stabilizing the existing base course using a mix of dune sand and manufactured sand with a small percentage of Portland cement. Compaction, unconfined compressive strength and California bearing ratio tests were conducted on the treated sample with a varying cement proportion of 0%, 3%, 5% and 7% by weight. The first tests were done with a mix of 50% dune sand and 50% crushed sand that is shown to have excellent results. For a more economic design, this study also included testing of another mix design with 70% dune sand and 30% crushed sand; laboratory results show this 70%/30% mixture was appropriate to use as a base-treated material for road construction material. This mix resulted in overall superior performance. Its use will reduce the cost of road construction by saving materials and time, and it will also have lower environmental impacts in desert areas. This study has shown that the stabilization of weak material (desert sand) by using cement improves the strength characteristics of the treated 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".