Use of Ground Penetrating Radar to Determine an In-Situ HMAC Surface Course Lift Thickness Profile: A Case Study - Highway 401, Trenton, Ontario
Why this work is in the frame
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Bibliographic record
Abstract
The Ontario Ministry of Transportation (MTO) retained Stantec Consulting to complete a Ground Penetrating Radar (GPR) survey with the intent of identifying the thickness of the surface course on Highway 401, from 500 m west of the Trent River Bridge, in Trenton Ontario, westerly 11.7 km. The GPR survey was completed in the eastbound and westbound travel lanes to determine the pavement layer surface course profiles. The surface course on this section of Highway 401 consisted of a Dense Friction Course (DFC) which was delaminating at a number of locations. The application of GPR technology used on this project was considered non-conventional since the survey equipment was used to isolate the surface layer from the rest of the bituminous pavement layer. Additionally, core data was used to calibrate and validate the GPR thickness data. The GPR data was checked for quality and processed using RADAN 6.5, an advanced GPR data reduction software developed by GSSI. The GPR data was calibrated using ground truth information obtained by cores that were extracted along Highway 401 within the project limits. This paper will discuss the specialized GPR equipment setup and survey/analysis methods used for this case study project. Ultimately, a near continuous depth profile on the surface course asphalt was determined. Considering deficient lift thickness as the primary causation, this data was used to determine the extent of pressing repair needs which in turn was used to structure a preventative pavement intervention. For the covering abstract of this conference see ITRD record number 201211RT334E.
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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 | 0.000 | 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 | 0.001 |
| 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 it