Structuring Knowledge for AI-Driven Schedule Management in Digital Construction Twins

Abstract: Construction projects, which are prone to delays and budget overruns, struggle to transform fragmented data streams into coherent intelligence. We propose a novel approach to monitoring that combines onsite data capture, agentic workflows and knowledge graph architecture to provide a foundation for evidence-based progress tracking. This approach is built on an ontology for structured schedule management that unifies concepts from domain-specific ontologies and Autodesk Construction Cloud. Autonomous AI-driven agents enable knowledge evolution by interacting with a knowledge graph. This lays the foundation for a living digital twin that adapts as new data emerges, supporting consistency checking and cross-system integration.