Analysis of center of gravity roller drum soil stiffness on compacted layered earthwork
Bibliographic record
Abstract
This paper describes the interpretation of intelligent compaction (IC) data from two layered soil test beds using center of gravity (CG) roller-measured soil stiffness. Conventional edge-mounted (EM) roller-measured soil stiffness values are interpreted from vertical accelerations measured by discrete, single-position accelerometers. However, these EM accelerometers are located at variable distances from the drum CG; the resulting vertical accelerations are thus affected by the rotation of the roller drum about the drum CG in the direction of roller travel. This leads to undesirable measurement artifacts, specifically multiple possible soil stiffness values for one soil location and an artificial dependence of the soil stiffness values on the direction of roller travel. In this study, left and right EM acceleration data from a vibratory roller are used to compute vertical accelerations at the CG of the roller drum. These vertical accelerations are used to compute CG stiffness values, which are not subject to the measurement artifacts associated with EM stiffness values. The resulting CG stiffness values are used to interpret IC data from two test beds with multiple 15–30 cm thick base–subbase–subgrade lifts. CG soil stiffness increases with the addition of subbase and base lifts, showing a desired sensitivity to changes in soil materials. CG stiffness also increases with the addition of multiple base lifts, showing a desired sensitivity to an increase in the overall thickness of the base material. This study demonstrates the efficacy of this unambiguous measure of soil stiffness for practical usage in IC of layered earthwork systems.
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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 | 0.001 |
| 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".