Performance estimation of a shallow foundation on an unsaturated expansive soil slope subjected to rainfall infiltration
Bibliographic record
Abstract
In the last two decades, there has been a significant increase in infrastructure development on slopes of hilly regions of the world, due to population growth. There are many infrastructures on unsaturated expansive slopes, especially in semi-arid and arid regions. Rainfall infiltration is one of the major factors that contributes to the slope and infrastructure foundations failures on hilly slopes with unsaturated expansive soils. In the current study, a rational approach is proposed considering the combined influence of the foundation-slope behavior based on the principles of unsaturated soil mechanics. This is achieved by a novel numerical modelling approach using the commercial software Geo-studio to investigate the performance of strip foundation located on the top of the unsaturated expansive soil slope subjected to various rainfall infiltration conditions. Hydro-mechanical coupling analysis is conducted to evaluate the rainfall water infiltration influence combined with slope stability analysis using limit equilibrium method. Comparisons are made between both the foundation bearing capacity, slope stability before and after rainfall water infiltration. Different failure mechanisms of the foundation and slope system are presented with and without foundation loading for various rainfall scenarios. Results summarized in this paper are helpful for the geotechnical engineers for understanding the performance of shallow foundations on unsaturated expansive soil slopes considering the influence of rainfall infiltration conditions.
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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".