With the rapid development of global road transportation, countries worldwide have completed the construction of road networks. However, the ensuing challenge lies in the maintenance of existing roads. It is well-known that countries allocate limited budgets to road maintenance projects, and road management departments face difficulties in making scientifically informed maintenance decisions. Therefore, integrating various artificial intelligence decision-making techniques to thoroughly explore historical maintenance data and adapt them to the context of road maintenance scientific decision-making has become an urgent issue. This integration aims to provide road management departments with more scientific tools and evidence for decision-making. The framework proposed in this paper primarily addresses the following four issues: 1) predicting the pavement performance of various routes, 2) determining the prioritization of maintenance routes, 3) making maintenance decisions based on the evaluation of the effects of past maintenance, and considering comprehensive technical and management indicators, and 4) determining the prioritization of maintenance sections based on the maintenance effectiveness and recommended maintenance effectiveness. By tackling these four problems, the framework enables intelligent decision-making for the optimal maintenance plan and maintenance sections, taking into account limited funding and historical maintenance management experience.

翻译：随着全球道路运输的快速发展，世界各国已基本完成道路网络的建设。然而，随之而来的挑战在于现有道路的养护问题。众所周知，各国用于道路养护项目的预算有限，道路管理部门在制定科学养护决策时面临诸多困难。因此，整合多种人工智能决策技术，深入挖掘历史养护数据，并将其应用于道路养护科学决策的特定情境，已成为一项亟待解决的问题。这一整合旨在为道路管理部门提供更科学的决策工具与依据。本文提出的框架主要解决以下四个问题：1）预测各路段的路面性能；2）确定养护路段的优先顺序；3）基于历史养护效果评估，并综合考虑技术与管理的综合指标，制定养护决策；4）依据养护效果及推荐养护效果，确定养护路段的优先顺序。通过解决上述四个问题，该框架能够在有限资金和历史养护管理经验的约束下，实现最优养护计划与养护路段的智能决策。