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In order to investigate the key factors and analyze their effects on maintenance and rehabilitation (M&amp;R) strategies, data for 2495 pavement sections were collected from the pavement management system (PMS), including pavement performance data, traffic data, material property data, and M&amp;R record data. Logistic regression was first employed to explore the influential factors on maintenance probability. Afterward, the classification tree model was established to find out the key factors on resurfacing thickness. Results showed that road sections with higher IRI, rutting depth (RD), deterioration rate of surface friction coefficient (DRSFC), pavement patching ratio (PPR), and transverse cracking severity index (TCSI) before treatment had significantly higher maintenance probability, which could be quantified by the developed logistic model. Moreover, treatments implemented on bridge decks tended to have greater resurfacing thickness. For pavement M&amp;R projects, with the tensile strength ratio (TSR) of top layer materials higher than 88.7% and pretreatment SFC higher than 49, the resurfacing thickness would be thinner. For bridge M&amp;R projects, middle layer TSR higher than 88.3% led to thinner overlays, and much thinner resurfacing thickness can be observed if pretreatment RD was less than 8.72 mm. When middle layer TSR was lower than 88.3% and pretreatment IRI was higher than 2.383 m/km with larger AESAL, the resurfacing thickness would probably be the thickest. The two models built in this paper provided probabilistic estimation of maintenance probability and explored key factors together with their critical split points for resurfacing thickness, which could be regarded as an alternative decision-making tool for pavement engineers.

Factors Affecting Maintenance Probability and Resurfacing Thickness Based on the Pavement Management System