Field study on intelligent compaction for compaction quality control of subgrade bases
Bibliographic record
Abstract
To address the challenges in adopting intelligent compaction as the primary method for compaction quality control, this study investigated methods for determining appropriate target intelligent compaction measurement values (ICMVs) for compaction quality control and strategies to manage compaction quality considering the ICMV variability. Field tests revealed that the mean compaction meter value (CMV) increased with the number of roller passes. However, a high coefficient of variation was observed across all roller passes, indicating significant local variability in compaction quality. A 5 m region of interest was determined optimal for correlating CMV with plate-load-test results and determining CMV for compaction quality management. Uniform compaction could not address localized variability in compaction quality. Detecting weak areas during the compaction process and concentrating efforts in these regions improved the uniformity of the compaction quality. This study provides valuable insights for ICMV-based compaction quality control, assisting construction supervisors in setting target ICMVs, and developing effective strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".