Identification of Collapsible Soil Using the Fall Cone Apparatus
Bibliographic record
Abstract
Abstract Soils that go through a great loss of volume upon wetting with or without additional loads are identified as collapsible. In recent years, there has been an increasing awareness of this type of soil due to the expansion of urban developments to arid regions. Also man-made earth structures often exhibit collapsing behavior when compacted at water content less than the optimum moisture content. In the literature, methods can be found to predict this behavior based on field and laboratories test results. These methods, however, are time consuming and developed for the type of soils tested. This paper presents the results of an experimental investigation on collapsible soils using the fall cone and the oedometer apparatuses. The fall cone method, originally developed to determine liquid and plastic limits of soils, was adopted in this investigation to identify its collapse potential. A cone penetration limit (Plim) is introduced to identify collapsible soil and a correlation between the collapse potential, CP, and the cone penetration, P, was developed and validated with the present experimental results and those available in the literature. Furthermore, a simple procedure is introduced to determine the optimum Proctor Moisture Content for collapsible soils from the results of the cone test. The proposed procedure is simple and fast to evaluate soil collapsibility by a single reading of the cone penetration.
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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 | 0.002 | 0.001 |
| 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".