arXiv — Machine Learning · · 4 min read

Repair Lot Skyline: A Weighted Constraint Satisfaction Approach to Pavement Repair Optimization from Geospatial Hazard Density

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Computer Science > Machine Learning

arXiv:2610.06989 (cs)
[Submitted on 4 Oct 2026]

Title:Repair Lot Skyline: A Weighted Constraint Satisfaction Approach to Pavement Repair Optimization from Geospatial Hazard Density

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Abstract:Pavement agencies must translate a spatially distributed distress inventory into a bounded, actionable repair-lot plan: accident-critical defects (potholes) must always be addressed, lower-risk defects (cracks) should be included only when their benefit justifies the repair cost, and historical patch locations signal re-degradation risk without themselves triggering repair. We formalize this as a Repair Lot Skyline problem: a Weighted Constraint Satisfaction Problem (WCSP) defined over chainage (distance along the road) rather than over time, so that it requires only a single-epoch distress survey and makes no claim about future deterioration. The WCSP identifies 143 candidate hazard clusters (61 hard, 82 soft), of which 106 are merged into a final repair plan totaling 1,997.4 m---83.7% of the 2,385.9 m that would be required if every soft candidate were included regardless of cost. This plan covers 100% of observed potholes (138/138) and 91.6% of observed cracks (404/441), capturing 93.6% (542/579) of the total hazard benefit available in the full candidate set. The skyline frontier shows pronounced diminishing returns beyond this point: the remaining 37 excluded soft candidates would add only 6.8% additional benefit for a 19.4% increase in repair length. We further formalize the minimum-lot-length $L_{\min}$ and historical-context radius $\kappa$ as a joint, four-objective hyperparameter search over this WCSP; on the same case study, the recommended configuration ($L_{\min} = 14.7$ m, $\kappa = 10$ m) reduces repair-crew mobilizations by 7.1% relative to an untuned default, at the cost of a 12.9% larger budget and a 1.1-percentage-point lower crack coverage.
Comments: 30 pages, 7 tables, 5 figures
Subjects: Machine Learning (cs.LG)
ACM classes: G.3; J.2
Cite as: arXiv:2610.06989 [cs.LG]
  (or arXiv:2610.06989v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2610.06989
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Takato Yasuno [view email]
[v1] Sun, 4 Oct 2026 07:16:45 UTC (317 KB)
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