Modulus−suction−moisture relationship for compacted soils
Bibliographic record
Abstract
The ultimate parameter of interest in engineering design of compacted subgrades and support fills for highways, railroads, airfields, parking lots, and mat foundations is often the soil modulus. Modulus of compacted soils depends not only on dry unit weight and moisture but also on matric suction and soil structure (or fabric) resulting from the compaction process. However, these relationships in the as-compacted state (i.e., immediately after compaction) have not yet been extensively explored. This paper presents an experimental laboratory study of the shear modulus – matric suction – moisture content-dry unit weight relationship using three compacted subgrade soils. Compacted subgrade specimens were prepared over a range of molding water contents from dry to wet of optimum using enhanced, standard, and reduced Proctor efforts. A nondestructive elastic wave propagation technique, known as bender elements, was used to assess the shear wave velocity and corresponding small-strain shear modulus (G o ) of the compacted subgrade specimens. The matric suctions were measured with the filter paper method. An empirical relation that takes into account the effect of compaction conditions is proposed for the G o – matric suction – molding water content relationship of compacted subgrade soils.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".