Independent determination of compactometer value from a compaction roller
Bibliographic record
Abstract
Continuous Compaction Control (CCC) enables near-continuous monitoring of the compaction process during earthwork construction by instrumenting a compaction roller with sensors. One of the most widely used CCC measurements is the Compactometer Value (CMV), due to its historical significance and relatively straightforward calculation procedure, which involves performing spectral analysis of the compaction drum’s vibratory response. In practice, CMV measurements from commercial CCC systems are typically black box in nature, as they report only the final values—without disclosing the raw acceleration data or the calculation steps performed by the CCC system. As a result, most CCC technologies remain closed and proprietary. This paper presents a methodology to independently calculate CMV using data collected from a compaction roller equipped with both an aftermarket CCC retrofit kit and a secondary accelerometer. This setup allowed for direct comparisons between independently measured CMV values and those reported by the CCC retrofit kit. Data was collected from the construction of a full-scale earthen embankment compacted in multiple lifts. Results from this study showed that independently measured CMV values were within 1% of CMV values measured from the retrofit kit. These results provide a practical, transparent workflow for researchers and practitioners interested in replicating CMV measurements, ultimately contributing to the development of more open-source CCC solutions. • Compactometer value (CMV) was independently calculated during soil compaction. • Independent CMV values were compared against CMV values measured from a CCC system. • Independent CMV values were consistent with commercially measured CMV values. • Results from study may help increase the adoption rate of CCC technologies.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 0.002 |
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".