  Due to limited budgets allocated for road maintenance projects in various
countries, road management departments face difficulties in making scientific
maintenance decisions. This paper aims to provide road management departments
with more scientific decision tools and evidence. The framework proposed in
this paper mainly has the following four innovative points: 1) Predicting
pavement performance deterioration levels of road sections as decision basis
rather than accurately predicting specific indicator values; 2) Determining
maintenance route priorities based on multiple factors; 3) Making maintenance
plan decisions by establishing deep reinforcement learning models to formulate
predictive strategies based on past maintenance performance evaluations, while
considering both technical and management indicators; 4) Determining repair
section priorities according to actual and suggested repair effects. By
resolving these four issues, the framework can make intelligent decisions
regarding optimal maintenance plans and sections, taking into account limited
funds and historical maintenance management experiences.
