Effect of Utility Cuts on Serviceability of Pavement Assets - A Case Study from the City of Calgary
Bibliographic record
Abstract
The City of Calgary has a road network of nearly 16,000 lane-kilometres with an asset value of about $10.0B. With such an extensive roadway network, it can be very expensive and disruptive to carry out maintenance activities on sections affected by utility cuts. Pavement engineers generally agree that utility cuts negatively impact the integrity of pavement structure and reduce the serviceability of pavements. Therefore, a forensic investigation was conducted to determine the level of impact on the serviceability of pavements in Calgary due to the utility cuts. The impact was evaluated separately for arterial, collector, and local roads. Additionally, for each road class pavements in different age groups were evaluated. A total of 72 sections (24 for each road class) from different geographic locations in the city were selected to provide statistically significant data. Each of the 72 sections consisted of a sample unit with a utility cut and an adjacent sample unit without a utility cut. In order to quantify the impact of the utility cuts on the pavement performance, a comprehensive field investigation plan was developed. Field investigation included the following testing being carried out for both sample units in each section: Visual distress evaluation; Falling Weight Deflectometer testing; Ground Penetrating Radar survey; and Roughness survey using SurPRO. The results from the sample units with cuts were analyzed and compared with the results from sample units without cuts, to determine the loss in pavement service life due to the presence of a cut. Statistical analyses including ANOVA were carried out to confirm the research hypothesis. A fee structure was also developed for each of the three road classes and for the different age groups. This paper discusses and summarizes all the above research activities. Results of the entire study are included and the conclusions presented at the end.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".