Quality Control of Asphalt Pavement Field Compaction Using Field-Measured Pavement Permeability
Bibliographic record
Abstract
For decades, the use of rotary steel drum compaction train to compact asphalt concrete and the use of density as a quality control criterion for job acceptance have been the mainstream practices.These have remained essentially unchanged since their respective adoptions by the industry.Hence, asphalt material properties have been overemphasized with no consideration of the impact of construction processes.Limiting the intrusion of water into the body of asphalt concrete pavement has been an age-long recommendation.This was found to reduce the potential of asphalt concrete moisture damage susceptibilities.However, attempts to reliably measure asphalt pavement permeability in the field or correlate it to other surrogates have been unsuccessful or at least unreliable, thus, frustrating the applicability of permeability measurements.This thesis seeks to solve the aforementioned problems by measuring asphalt pavement permeability in the field and relating the measured permeability coefficients to different construction factors.The study also compares the rotary steel drum compaction technologies and the AMIR to highlight the effects of different compaction methods on the properties of asphalt pavements with a focus on permeability as an alternative quality control property.Ten sites were selected for the field compaction used in this thesis to study the effects of different field compactors on asphalt pavement compaction and permeability.Case one of the field compaction studies involved the use of vibratory train and AMIR in nine projects.Case two involved the use of the vibratory and oscillatory trains and AMIR compactors in one project.The results of field compaction and permeability using the rollers indicate that AMIR compactor yields asphalt pavement surfaces with lower permeability at a
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".