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Record W214498535

EVALUATION OF HOT-MIX ASPHALT SAMPLING TECHNIQUES

2007· article· en· W214498535 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCivil engineering studies. Transportation engineering series · 2007
Typearticle
Languageen
FieldEngineering
TopicTransport Systems and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsSample (material)DocumentationSampling (signal processing)Agency (philosophy)Transport engineeringEngineeringTruckChristian ministryCivil engineeringOperations managementComputer science
DOInot available

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.260
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it