Bibliographic record
Abstract
Compaction curves (or densitymoisture relationships) of cohesive soils are essential components for establishing practical and reliable criteria for effective control of field compaction. In this paper, modules built from empirical models for simulating the compaction curves of cohesive soils based on easily measured basic soil properties and compaction energy were developed using both statistical regression and artificial neural networks (ANNs) techniques. A large number of compaction curves pertaining to a wide variety of fine-grained soils were collected and used in modeling. The developed modules were able to predict compaction curves of soils with good accuracy, with the ANN-based module outperforming the statistical-based analog. The compaction modules were utilized to inquire about the compactibility behavior of fine-grained soils in relation to their properties and the compaction energy used. Besides their use as independent compaction curve predictors, the compaction modules can be used as supplementary units in numerical models for solving geotechnical engineering problems and as tools useful in preliminary design phases and feasibility studies.Key words: cohesive soils, compaction curve, modeling, neural networks, regression.
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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 | 0.003 |
| 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) | 0.001 | 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".