This dissertation presents a novel mathematical framework to construct an optimal road asset management system considering condition inspection policies and information value. Accurate and frequent inspection is an essential component of improving the efficiency of maintenance policy and reducing a significant amount of social costs. Accordingly, various road asset inspection technologies have been developed. However, most previous studies have excluded consideration of inspection techniques and strategies in optimizing the road asset management system. They have mainly focused on a bi-level problem with deciding on maintenance activities and allocating a pooled budget. This dissertation covers two topics related to inspection strategies in road asset management systems. In the first part, an optimized road asset management system is established based on the current road asset inspection technology. In order to achieve that, we integrate multi-scale decisions, including segment-level maintenance policy, group-level inspection strategy, and network-level budget allocation. In the second part of the dissertation, we propose a new road asset management system according to the future innovation in inspection technologies and the increase in market share of connected vehicles, and evaluate its potential benefits. We conduct a numerical study using a real-world roadway network in South Korea. Results of the numerical analysis confirm that the optimal maintenance policy involving inspection strategies has the lowest social costs, and show the effectiveness of the real-time road asset monitoring system using connected vehicles.