A Numerical-Hierarchical Framework for Predicting Volume Changes in Expansive Soils under Variable Surface and Weather Conditions
Bibliographic record
Abstract
This research developed a numerical-hierarchical framework that captured surface conditions and climate parameters. Volume changes under distinct scenarios of surface boundary, antecedent moisture, and meteorological parameters were predicted using a coupled seepage-deformation model. Risk was hierarchically based on expert judgment for surface scenarios (Stage-I indices) and normal distribution for antecedent moisture and atmospheric parameters scenarios (Stage-II indices). Results indicated seasonal volumetric changes with minor variations of −5 mm from January to April, a steady settlement of −17 mm by June, and a gradual heave of +8 mm by December. All Stage-I indices showed similar trends such that the fluctuations were highest for vegetation, followed by slope, then by cover, and lowest for loading. Volume changes gradually reduced with depth and diminished at 3.1 m. Similar seasonal and profile trends were generally found for most Stage-II indices. Nonetheless, different trends under wet and dry conditions were observed for initial water content, precipitation, and air temperature. For the datum scenario, risk was non-existent till February, increased to 2.3 by June, diminished by October, and rose back to 1.0 by December. Similar values of cyclic variations in risk were found in most urban facilities. Volume changes were found to be two times higher in parks, insignificant for roads, half for five story buildings, and one-fourth for pipes under roads. Among the Stage-II indices, risk for the initial water content inhibited seasonal variations whereas that for precipitation was about half with a wider distribution; all the other indices showed about one-third the values. Under a higher occurrence probability of 0.129, a magnified risk was observed for all the indices such that the most critical were the initial water content and precipitation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".