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 not been successful or at least reliable, 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 with less permeable surfaces at a comparable level of compaction with the vibratory and oscillatory trains compaction methods. AMIR compactor was also found to reduce the mean permeability of the surfaces compacted on concrete base compared to the conventional rotary steel drum compactors and was also more efficient. Also, this thesis further presents new developments in field measurements of permeability. Based on laboratory trials and calibrations that employ the electronic water level sensor system, a new device termed "Carleton Permeability Device" is developed and proposed.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".