Geometric Fidelity Uncertainty in Cognitive Digital Twins: Automated Point Cloud Completion, Tessellation, and IFC Recording for Maintenance

Abstract Geometric fidelity is a fundamental prerequisite for reliable cognitive digital twins (CDTs). An incomplete as-is geometric foundation leads to model uncertainty, thereby undermining CDT reasoning and maintenance decisions. However, point cloud data acquired through laser scanning is inevitably subject to occlusions caused by structural obstructions, sensor limitations, and complex MEP configurations. Existing methods fail to address the high-precision full spectrum of occlusion scenarios in real-world Scan-to/vs-BIM workflows, particularly for border truncations and irregular non-Manhattan geometries. This paper proposes a method that systematically reduces geometric uncertainty in CDT construction through three stages. First, a gap classification scheme categorises incomplete point clusters by occlusion severity and object class, enabling targeted treatment of each scenario for both Manhattan and non-Manhattan geometries. The completed clusters are then tessellated into high-resolution meshes and converted into IFC models through a new purpose-designed data structure that embeds laser-scan timestamps and as-designed global identifier links. Together, these stages form a closed-loop geometric DT updating system that continuously reflects the as-is physical state of the building, directly reducing the geometric uncertainty that would otherwise propagate into CDT-based maintenance reasoning and risk assessment. The method is validated on various real-world datasets. The performance is evaluated with ground truth and Chamfer distance, with results benchmarked against LOA requirements to confirm practical ability. The resulting timestamped IFC models provide a geometrically reliable foundation for CDT-based predictive maintenance and facility risk assessment.