The behavior of Oil-Contaminated Sands in CBR Test
Bibliographic record
Abstract
Several factors affect the bearing of a load of natural and engineered embankments and slopes including the crude oil leakage, leading to a severe decrease in resistance. This is especially important for oil-rich countries, such as Iran, which have several crude oil resources. The main purpose of this study is to investigate the load-bearing (i.e., load versus settlement) and strain-stress behavior of crude oil contaminated sandy soils using the California bearing ratio (CBR) test. In this paper, 10 different types of sand with different characteristics were used. At first, a series of CBR experiments were performed for natural sands (i.e. clean sands) and then contaminated sands with 6% crude oil tested under similar conditions and densities to obtain the reduction in bearing a load of crude oil-contaminated sands quantitatively. Experimental results showed that bearing the load of sand containing 6% of crude oil decreased at least 50% compared to clean sands and the stress-strain diagram of these contaminated soils would decrease significantly. Based on the results of the investigation, it can be stated that particle shape (sharpness or roundness), coarse particle ratio, and finally the type of aggregation influences the resistance of crude oil contaminated sands. It was also found that standard Ottawa sand had an 83% reduction in strength and sand with a coarse particle had a 57% decrease in strength. Sand contaminated with crude oil experienced a severe loss of bearing capacity, so in designing foundations and engineering structures, greater safety factors should be considered, where there is a risk of crude oil leakage.
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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 | 0.001 |
| 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".