Mineralogical, Chemical, Geotechnical and Mechanical Investigations of Bafang Lateritic Fine Soils Formed on Basalts (West-Cameroon) for Road Embankment Purpose
Bibliographic record
Abstract
The present paper treats the relationship between geotechnical parameters and the mineralogical compositions, in order to understand the behavior of the Bafang lateritic fine soils and their efficient use as road embankment materials on the other hand. Thus, the field campaigns have permitted to distinguish two facies of Bafang lateritic fine soils: reddish and yellowish facies. The X-ray diffraction (XRD), the thermo-gravimetry analysis (TGA) and the X-ray fluorescence (XRF) have revealed that the reddish facies contents: kaolinite (53.34% -48.29%); hematite (16.62% - 17.40 %); gibbsite (8.26 % - 16.54%), ilmenite (7.6 % -7.98%), quartz (1.92 % - 4.65 %), illite (2.65% - 1.99 %) and accessories minerals as florencite (1.45%); montmorillonite (0.90 %) and plagioclase (0.69 %); while the yellowish facies is composed of : 65.1 % kaolinite, 11.2 % gibbsite, 9.64 % goethite, 7.02 % quartz, 3.23 % ilmenite and 2.08 % of illite. Their Silica/sexquioxide ratios correspond to those of moderate laterite. The geotechnical and mechanical characteristics have shown that the reddish and yellowish facies are respectively silty clay and sandy silt. They are plastic to very high plastic and moderately clayey to clayey materials. Moreover, they are medium swelling to high swelling. The IBR values more than 40% and the soaked CBR values prove that these materials belong to the S4 bearing capacity class. The minimum values of UCS and Rt are respectively 1.7 MPa and 0.17 MPa. Then, the results of geotechnical and mechanical properties combining to those of XRD, TGA and XRF have demonstrated that the fine lateritic soils with low silica/sexquioxide ratios values (less than 1.6) are suitable for road embankment.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".