Optimization of pavement maintenance and rehabilitation using pavement management system in Prince George
Bibliographic record
Abstract
,Pavement Management Systems (PMS) are essential for guiding cost-effective and sustainable road maintenance, particularly in municipalities operating within harsh climates and under financial constraints. This research examines the optimization of pavement maintenance strategies for the City of Prince George, British Columbia, by combining historical condition data, predictive modeling, and decision-support frameworks. The study utilizes pavement distress survey results from 2016, 2017, 2020, and 2023 to assess network-level deterioration, identify critical distress types, and establish performance baselines. To forecast pavement performance, three modeling approaches—Random Forest (RF), Multiple Linear Regression (MLR), and Artificial Neural Networks (ANN)—were applied to predict the Pavement Distress Index (PDI). These models were evaluated using the statistical metrics such as Root Mean Square Error (RMSE), and coefficient of determination (R²). The Random Forest model achieved the highest predictive accuracy (R² = 0.96, RMSE = 0.55), followed closely by the ANN (R² = 0.95, RMSE = 0.48), while the MLR model demonstrated lower predictive capability (R² = 0.81, RMSE = 0.92). Variable importance analysis identified transverse cracking, rutting, and surface roughness as the most influential predictors of deterioration. The findings of this research provide a data-driven framework for proactive pavement maintenance planning in Prince George, enabling the prioritization of high-impact interventions and the optimization of rehabilitation budgets. By extending pavement service life and reducing long-term maintenance costs, the proposed methodology supports the creation of more resilient transportation infrastructure. The framework can be adapted for use in other municipalities facing similar environmental and operational conditions, strengthening the integration of advanced analytics into municipal asset management practices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".