Using Pavement Management System Concepts to Determine the Cost and Impact of Utility Trenching on an Urban Road Network
Bibliographic record
Abstract
The results are presented from a 2-year study on the impact of utility trenching on urban flexible road network pavement roughness, pavement surface distress, structural carrying capacity, pavement life cycle, rehabilitation and maintenance requirements, and the costs associated with these impacts in Ottawa-Carleton, Canada. Included are the pavement performance and life-cycle relationships developed using modified methodologies to address concerns raised by reviews carried out by Construction Technology Laboratories Inc. and the National Research Council of Canada on utility trenching studies to date. In this study, normalized individual pavement section life cycle, a composite pavement quality indicator, and performance prediction models calibrated with numerous years of field data were used in the life-cycle and pavement performance determination. These modifications made to the conventional pavement management system when used to determine the impact of utility trenching are shown to provide performance and life-cycle relationships with better correlation than algorithms used in the previous studies. In this study, very high coefficients of determination ( R 2 ) of 0.79 to 0.85 were obtained for the pavement performance and life-cycle relationships regressed from field data for quantification of urban road network pavement with and without the impact of utility trenching. The factors and costs associated with the impact of utility trenching, such as reduction in pavement life cycle, additional cost for subgrade base repair, pavement strengthening requirements from loss of fatigue structural carrying capacity, and the additional area affected beyond the trenched area, are also quantified in this study.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".