Density Measurement Methods for Acceptance of Bituminous Mixtures: Survey of Practice
Bibliographic record
Abstract
A survey of all U.S. state and Canadian provincial departments of transportation (DOTs) was conducted to gather information about current practices related to acceptance of bituminous mixtures based on field density measurements. The objective of this paper was to document and synthesize the current methods used by each DOT and determine its perspective on the current/future use of nonnuclear gauges. A total of 34 responses were received. In addition, an Internet search was performed to determine the methods used by those U.S. state DOTs that did not respond. Four in-situ bituminous mixture density measurement methods were identified through the survey: collection of cores/plugs only, nuclear density gauges only, nuclear density gauges plus cores/plugs, and nuclear/nonnuclear density gauges plus cores/plugs. In-place core density measurements were found to be the most common method for accepting density. Some regional trends were observed in the responses. More than 50% of the respondents have investigated the use of nonnuclear density gauges. Responses from states that use nonnuclear gauges were grouped together and compared to identify common factors in allowing these gauges. Two important conclusions can be drawn from this study: (1) given the variation in responses, some of which were contrary to published research, there is a need for a coordinated national study investigating the methods used to determine and accept in-situ bituminous mixture density, and (2) there is a general interest in employing nonnuclear technologies for bituminous mixture density acceptance.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".