Bibliographic record
Abstract
Insuring the integrity and security of hot mix asphalt (HMA) samples is critical to assuring the quality of the installed product and complying with Federal requirements. Samples of HMA are often taken at the plant with limited state supervision. Further, samples are taken from a truck where obtaining a representative sample can be difficult. The concept of moving the sample location to the job site offers the potential to address the weaknesses cited above. However, there are a number of different approaches, each with advantages and disadvantages. The objective of the proposed research project was to produce a review of successful methods and practices currently used to sample HMA during production and installation. This included visiting other states and providing detailed documentation of the visits. While achieving this objective, sufficient data were collected to allow Illinois Department of Transportation's (IDOT’s) personnel to draw a final recommendation for the optimum technique to be adopted for HMA sampling in future projects. During the course of this project, sampling practices in six highway agencies were evaluated (Kansas, Iowa, Ohio, Michigan, Florida, and Ministry of Transportation of Ontario). Four of these agencies specify roadway sampling, while one agency is experimenting with a new generation of mechanical sampling device and another agency samples directly from a Material Transfer Device (MTD). During the course of this project, areas of improvement in the current Illinois QC/QA program were also identified. In general, sampling behind the paver is being conducted by many states without much difficulty. Based on the site visits conducted in this research, the TRP group determined that the roadway sampling procedure adopted by Michigan Department of Transportation (DOT) is the most appropriate for possible implementation in Illinois. In addition to this sampling technique, sealed bags adopted by Iowa DOT may be used, if necessary, to safely transport samples from the field to lab. Results of this research project also indicated that all visited states have a much higher sampling/testing frequency than Illinois and have successfully implemented an incentive/disincentive specification system. In addition, all visited states comply with the FHWA Technical Advisory (TA) or are in the process of making changes to comply with the TA. Based on these findings, the TRP has determined that the current Illinois QC/QA program is in need of several modifications to ensure successful implementation of roadway sampling, to comply with the TA, and to encourage high-quality construction of HMA. While changing sample location would improve sample security, it would not address shortcomings of the existing QC/QA program. In conjunction with implementation of roadway sampling, it is recommended to base sampling on tons instead of time, that IDOT personnel determine random sampling locations, witness samples taken, and take immediate possession of samples; adopt incentive and disincentive pay; and accept density based on field cores. It is also recommended that the formed TRP group continue effort in revising the QC/QA program to gain compliance with the TA and to introduce changes deemed necessary from our field visits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.001 | 0.001 |
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".