Integration of Preventive Maintenance in the Pavement Preservation Program: Ontario Experience
Bibliographic record
Abstract
Traditional pavement preservation (PP) practices have mainly focused on corrective maintenance activities. However, with the constant demands on highway networks and the extensive costs required for rehabilitation, highway agencies have started to adopt preventive maintenance (PM) strategies into their PP programs. PM is a set of activities performed while the pavement is still in a good or fair condition to inhibit progressive failure and therefore extend the service life of the pavement. Potentially, PM can enhance pavement performance and reduce the life-cycle costs of highway facilities. The Ministry of Transportation of Ontario (MTO) has been one of the pioneering agencies in applying pavement management system (PMS) analysis tools to its annual pavement maintenance and rehabilitation (M&R) program at the network level. Currently, MTO is in the process of implementing a PP program that includes PM as a key component. In this program, a practical PM model is developed through a set of dedicated decision trees (DT). This determines the feasible maintenance activities for each pavement section based on a number of factors, including existing pavement surface layer, condition, age, and traffic. The PM work program is finalized through budget optimization to determine the most cost-effective maintenance activity for each candidate section. The impact of the PM activities on the overall pavement performance is modeled as an immediate improvement in the pavement condition index and/or a slower rate of deterioration, depending on the nature of the PM activity. This impact is then accounted for and integrated with pavement rehabilitation analysis during the course of development of the final work program for the entire highway network. Budget analysis is performed to determine the impact of incorporating the PM activities into the PP program as compared to a PP program that includes rehabilitation activities only. Analyses results showed that under the same budget scenarios, incorporating PM into the overall PP program resulted in a significant improvement to the network condition. In this paper, an overview of the MTO PP program, with special emphasis on the integration of the PM program into the PMS, is presented. The development of PM DTs and performance modeling is discussed in detail. In addition, budget scenario analyses comparing the use of PM and M&R activities, as opposed to M&R activities only, in the development of the final work program, are presented.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".