Integrating digital construction workflow information with semantic web technologies

From a lean construction perspective, construction workflows (CWs) are assembly processes in which diverse entities—such as labor, equipment, and information—converge to produce the final output. Effectively managing CWs, especially under dynamic conditions, requires efficient information management to reflect evolving workflow situations and ensure coordination among stakeholders. While advancements in digitalization have improved data collection in CWs, current solutions often act as isolated "point solutions" with limited integration among systems. This fragmented data landscape restricts comprehensive insight into CWs, underscoring the need for effective data integration. Key challenges in integrating heterogeneous CW data include the need for formalized data models, effective data linking, and systematic utilization of integrated data. Existing approaches to CW information integration often lack a comprehensive data model to bridge heterogeneous data sources. Additionally, emerging implementations of imagery in the construction domain are frequently isolated from other digital information systems. Meanwhile, despite the potential of adopting Digital Twin concepts in construction, practical data modeling approaches to achieve DT information integration and interpretation are still needed. The aim of this research is to improve the integration of digital CW information by leveraging Semantic Web technologies. First, it develops a formal ontology suite to formalize and integrate heterogeneous CW information. Second, the ontology suite is extended to semantically interpret construction images, linking visual data with other digital CW information. Finally, it proposes a semantic digital twin framework that models and utilizes integrated CW data for monitoring purposes, enabling more effective predictive analytics and information interpretation. This work contributes to the body of knowledge by offering Semantic Web-based solutions that address research gaps in the formalization, linking, and utilization of digital CW information. The developed ontologies and framework enhance CW data interoperability and expand DT data modeling for monitoring CWs. This research lays the groundwork for broader adoption of digital solutions in construction to support the management.