A research team from Hosei University has developed an integrated data model that merges international construction and geospatial standards to streamline maintenance of Japan’s aging bridge infrastructure, according to findings published in Computer Aided Civil and Infrastructure Engineering.
The system combines Industry Foundation Classes (IFC), used for Building Information Modeling (BIM) in construction, with CityGML, a standard for representing three dimensional geospatial information. This integration enables unified management of both 3D geometric data and maintenance information including inspection results and repair histories in a single framework.
Professor Ryuichi Imai from the Faculty of Engineering and Design at Hosei University led the research team, which included Dr. Kenji Nakamura from Osaka University of Economics, Dr. Yoshinori Tsukada from Reitaku University, Dr. Toshio Teraguchi from University of Marketing and Distribution Sciences, and Dr. Chikako Kurokawa from Asia Air Survey Company Limited. Their findings were made available online October 5 and published in Volume 40, Issue 27 of the journal on November 14, 2025.
Japan confronts urgent challenges with aging infrastructure built during its rapid economic growth period. Extensive inspection data and repair histories have been managed disparately across paper ledgers or departmental systems, leading to inadequate integration between the experience of skilled engineers and digital data. The ineffective linking of on site expertise with vast amounts of digital information has hampered maintenance operations, particularly for bridges across the nation.
The research team’s solution addresses the separate and difficult management of bridges’ three dimensional geometry data and their maintenance information in siloed systems. The resulting framework enables what researchers describe as one source, multi use management that significantly streamlines maintenance workflows including inspection, diagnosis, and repair planning.
Imai explained that the work allows infrastructure managers, specifically local governments, to accurately grasp damage locations found during inspections and past repair histories for numerous bridges under their jurisdiction, all visualized on 3D models. Managers can instantly check information either on site or in the office, such as whether current damage is located in the same spot that was repaired five years earlier. This capability enables precise, data driven decisions about repair priorities and the most suitable repair methods, expected to improve infrastructure safety and longevity while ensuring efficient use of public funds.
The validation experiment was conducted using drawings, inspection records, and point clouds of bridges in Shizuoka City, demonstrating that the schema can be applied to 20 bridges of four types for three different use cases. The research forms part of Japan’s Strategic Innovation Promotion (SIP) Program Smart Infrastructure Management System, a cross ministerial initiative leveraging cutting edge digital technology to establish safer, more sustainable infrastructure management.
The team expects the integrated data model to be widely adopted as a standard by local governments nationwide within five to ten years, leading to the creation of digital twins for social infrastructure starting with bridges. On these digital twins, artificial intelligence driven deterioration forecasting simulations would become possible, accelerating the shift from reactive maintenance to predictive maintenance by repairing at optimal times before failures occur.
This transformation will help prevent critical accidents like bridge collapses and extend infrastructure lifespan, contributing to a society where people can live more safely and sustainably. During disasters, the system will enable immediate assessment of which bridges are passable, supporting rapid evacuation and recovery efforts.
Imai concluded that the technology, aimed at connecting field expertise with digital data and realizing future maintenance where infrastructure is collaboratively monitored across communities, can pave the way to a society where future generations can live more securely.
The research contributes to the growing body of work supporting digital twins and predictive maintenance, reinforcing the role of interoperable data in ensuring safe and resilient transport networks. While IFC provides detailed component level geometry and attributes needed for bridge structures, CityGML offers context of surrounding environments, making the combination particularly effective for comprehensive infrastructure management.
