From surveys to simulations: Integrating Notre-Dame de Paris' buttressing system diagnosis with knowledge graphs
Antoine Gros, Livio De Luca, Frédéric Dubois, Philippe Véron, Kévin Jacquot · Automation in Construction · 2024
The assessment of structural safety and a thorough understanding of buildings' structural behavior are critical to enhancing the resilience of the built environment. Cultural Heritage (CH) buildings present unique diagnosis challenges due to their diverse designs and construction techniques, often requiring attention during maintenance or disaster relief efforts. However, collaboration across CH and Architecture, Engineering, and Construction (AEC) fields is hindered by increasing information complexity and prolonged feedback loops. This paper introduces a methodological approach utilizing Knowledge Graph technologies to integrate structural diagnosis information and processes. The approach is applied to the diagnosis of the Notre-Dame de Paris buttressing system, demonstrated through a proof-of-concept knowledge system. By leveraging Knowledge Graph functionalities, insights are derived from the spatialization and provenance of mechanical phenomena, including observed or simulation-predicted cracks in mortar-bound masonry. • Observed and predicted mechanical phenomena are related through the Knowledge Graph. • Diagnosis insights are derivable from joint provenance and spatial resource indexing. • Pattern-based knowledge modeling enable standard AEC/CH ontology reuse. • Proposed Knowledge Graph methodological framework is evaluated with a proof of concept.