Improving the Characteristics of Subgrade Soil Using Different Chemical Additives: Case Study Al-Nasiriya Soil
Bibliographic record
Abstract
The roads in the city of Nasiriya in southern Iraq suffer from problems that occur as a result of repeated vehicle loads or due to weak soil and lead to losing their performance and being out of service despite their construction for a very short period. The use of chemical additives to improve the subgrade widely worldwide and give strength and durability to the weak soil while the possibility of using chemical additives for the substrate in Al-Nasiriya is still practically limited. The study aimed to verify the use of chemical additives (cement, lime, and ferric chloride) and to know their effect on the properties of Al-Nasiriya soil. The results showed a clear improvement in the UCS test when using chemical additives, and then the optimal percentages of additives were determined and were 9%, 10%, and 2% respectively, in addition to knowing the effect of the curing period (1, 7 and 14) days on the results of the test. For the other tests (maximum dry density, CBR, swelling, and optimum moisture content) were verified for the optimal chemical percentages and it was observed that the CBR values increased and the swelling values decreased after treatment and soaking in water for all additives, while the compaction parameters had a different behavior between the materials additive used.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".