Bridge maintenance in Japan is entering a period that demands new digital approaches. Many structures built during the country’s rapid economic growth era are now reaching advanced ages at the same time, creating pressure on local authorities responsible for their upkeep. Bridge inspection data, repair records and 3D geometry have long been managed in separate systems, often supported by paper ledgers or siloed databases. This lack of integration has limited the ability of engineers to combine field expertise with digital information. A new research project led by Hosei University proposes a way forward, introducing an integrated data model designed to support next-generation digital bridge maintenance.
Growing volume of inspection
Japan’s infrastructure managers face a growing volume of inspection findings and repair histories, yet information is frequently held in formats that are not interoperable. For many local governments, the absence of a unified digital environment means engineers must manually cross-reference data sets, slowing the decision-making process and making it difficult to track the progression of deterioration across thousands of bridge components. The need for a digital construction workflow that connects on-site knowledge with structured data is becoming increasingly clear, and it aligns with national priorities under the Strategic Innovation Promotion (SIP) Programme for Smart Infrastructure Management.
Addressing challenge
A research team led by Professor Ryuichi Imai from the Faculty of Engineering and Design at Hosei University has developed a model that seeks to address this long-standing challenge. Working with collaborators from Osaka University of Economics, Reitaku University, the University of Marketing and Distribution Sciences, and Asia Air Survey Co. Ltd., the team has designed a framework that brings together two international standards widely used in digital construction and geospatial modelling. Their findings were published in Computer-Aided Civil and Infrastructure Engineering in November 2025.
Data model
The proposed integrated data model merges the Industry Foundation Classes (IFC) standard, commonly used for Building Information Modelling (BIM) in the construction sector, with CityGML, a standard for representing and exchanging 3D geospatial information. While IFC provides the detailed component-level geometry and attributes needed for bridge structures, CityGML offers the context of the surrounding environment. By linking these standards, the researchers have created a structure that enables unified management of 3D bridge geometry data and maintenance information such as inspection results and repair histories.
Digital bridge asset management
This integration has the potential to support more efficient digital bridge asset management. Instead of switching between multiple systems, users can access information through a single data source, improving the accuracy of assessments and enabling faster interpretation of inspection findings. The ability to visualise damage locations on 3D bridge models allows maintenance teams to check whether deterioration is occurring in previously repaired areas and to analyse patterns that may inform long-term planning.
Unified digital format
Professor Imai explains that the approach is intended to support local governments overseeing large bridge inventories. With inspection cycles becoming more demanding, the ability to manage information in a unified digital format offers a clearer picture of asset condition. This can support decisions about repair priorities and methods, helping authorities use limited budgets more effectively. By grounding maintenance planning in structured and interoperable data, the model encourages consistent workflows and reduces the risk of missing critical information.
Digital twins
The researchers expect the proposed integrated data model to be adopted more widely over the next decade, potentially forming the foundation for digital twins of bridge networks across Japan. A digital twin, combining real-time condition data with 3D geometry, would enable simulations of future deterioration and support a shift from reactive maintenance to predictive maintenance. With predictive methods, engineers can repair assets at optimal points in their lifecycle, improving safety and extending the service life of bridges. The ability to forecast deterioration trends using AI techniques becomes possible when a robust, integrated data structure is already in place.
Beyond routine operations, the model may also support disaster response. In the aftermath of an earthquake or extreme weather event, a digital system capable of quickly aggregating inspection data and displaying the condition of each bridge would help authorities determine whether routes remain passable. This could contribute to faster evacuation and recovery planning, particularly in regions where bridges serve as essential links for emergency services.
Digital platforms
The research highlights the importance of connecting field expertise with digital platforms. Engineers on site bring knowledge that complements structured information. When these two sources are combined within an integrated data model, the potential for more informed decision-making expands significantly. The approach aligns with ongoing developments in digital construction, where interoperability and data standardisation are central to improving asset management outcomes.
Japan’s challenge with ageing bridges reflects a wider international trend. Many countries are exploring ways to integrate BIM, geospatial data and maintenance records into unified systems. The work undertaken by Professor Imai and his collaborators demonstrates a practical method for achieving this integration by using established standards already recognised in the digital construction sector.
Integrated data environments
As infrastructure managers consider how to strengthen maintenance strategies in the coming years, the adoption of integrated data environments is likely to become a priority. By enabling efficient management of 3D bridge geometry data, inspection results and repair histories, the proposed model offers a pathway toward more reliable infrastructure and more informed planning. 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 for future generations.
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