The thesis proposes an end-to-end approach for assessing productivity in Digital Twin Construction by integrating as-planned and as-performed data within a unified framework through the use of graph databases. It builds on existing methods by leveraging BIM models, transformation tools and RDF graphs to integrate and compare real-time performance with initial project plans for decision making and course-correction in construction project management. By utilizing graph databases, the research enables efficient querying and analysis of construction data. The methodology focuses on converting IFC models to RDF graphs to create a knowledge graph that links as-planned data with on-site, as-performed information. This process supports productivity analysis, allowing for more accurate comparisons between the planned and actual progress. The thesis demonstrates the viability of the approach through a case study, applying the methodology to a real construction project, where both planned and performed data were collected and analysed. This research addresses a critical gap in the construction industry by providing a comprehensive end-to-end solution for productivity assessment. The integration of data sources and continuous updates enhances the capability to detect deviations, recalibrate project timelines, and make informed management decisions, ultimately contributing to more efficient project execution and resource utilization.     «

The thesis proposes an end-to-end approach for assessing productivity in Digital Twin Construction by integrating as-planned and as-performed data within a unified framework through the use of graph databases. It builds on existing methods by leveraging BIM models, transformation tools and RDF graphs to integrate and compare real-time performance with initial project plans for decision making and course-correction in construction project management. By utilizing graph databases, the research ena...     »