Design of Foundations Built on a Shallow Depth (Less than 4 m) of Egyptian Macro-Porous Collapsible Soils
Bibliographic record
Abstract
It is nowadays well reported that collapsible soils spread in many countries, including United States, Russia, China, South America (e.g. Brazil), South and North Africa (e.g. Egypt, Algeria), Middle East (e.g. Saudi Arabia) and many countries in Eastern Europe. In general, collapsible soils are located in arid and semi-arid regions around the world. This special type of soil is characterized by abrupt reduction in strength, excessive and sudden settlement when it becomes wet leading to failure of the structure. Construction on such a kind of soil is one of the prominent problems in geotechnical engineering. The main objectives of this study are reporting geological and geotechnical zonation maps for potentially collapsible soils in inhabited areas in Egypt. Furthermore, a design technique for foundations built on a shallow depth of Egyptian macro-porous collapsible soils (less than 4 m) is developed. The design method includes a design chart for soil collapse field classification in terms of the most governing parameters, a method for foundation settlement estimation based on a correlation between the wetting-induced collapse strain and the applied pressure, and a design practice to guide practicing engineers to select the appropriate foundation system to construct on such soil with a great degree of confidence and safety.
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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 | 0.001 | 0.001 |
| 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".