Investigating effective maintenance policies for urban networks of residential cities by using optimum and sensitivity analyses
Bibliographic record
Abstract
Rural and urban networks have been rapidly expanding over time, so there is a rising demand for more optimum maintenance policies (MPs). The low traffic weights on the urban network of a residential city makes the appearance and density of load-associated distresses rare. This encourages the city’s transportation agency to depend on limited treatment choices and the subjective judgement of the agency’s engineers in managing maintenance and rehabilitation (M&R) activities. The engineers’ judgement reflects their extensive field experience and is usually used in this type of city rather than generating optimal computerized solutions. The present study proposes two alternative MPs that are more objective than the policy that depends on engineers’ judgement. One of the proposed alternative policies allows one M&R action along the planning horizon and the other allows multiple M&R actions. The optimum analysis showed that the preventive treatment is the optimal action for more than 50% of the network segments, which means that it plays a vital and nonnegligible role in improving the effectiveness of the M&R activities. Moreover, the sensitivity of the generated M&R plans to the initial performance of the pavement, traffic volume, and the objectives weights is investigated to determine the optimal time, suitable action, maintenance need, and optimal objective weights. The sensitivity analysis showed that varying objective weights leads to different optimal and rational solutions and the most cost-effective solution is not achieved at equal weights for objectives.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".