Estimating compaction behavior of fine-grained soils based on compaction energy
Bibliographic record
Abstract
For successful designs of geotechnical structures, rational determination of the engineering properties of soils is an important process. In this context, compaction parameters, maximum dry unit weight (γdmax), and optimum water content (wopt) are required to be determined at various compaction energies. This paper proposes correlation equations that relate γdmax and wopt obtained from standard Proctor (SP) and modified Proctor (MP) tests to the index properties. To develop accurate relations, the data collected from the literature and the authors’ own database have been used. It has been found that while wopt has the best correlation with plastic limit (wp), γdmax can be estimated more accurately from wopt than it can from wp. In addition, the empirical methods including compaction energy (E) are described for estimating wopt and γdmax of fine-grained soils. The variables of the developed models for wopt and γdmax are wp, E, and wopt. It has been shown that the proposed correlations including the compaction energy will be useful for a preliminary design of a project where there is a financial constraint and limited time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.000 | 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".