Framework for the Verification of Geometric Digital Twins: Application in a University Environment

Digital Twins rely on accurate geometric models to ensure reliable representation, yet maintaining and updating these models remains a persistent challenge. This paper addresses one aspect of this challenge by focusing on the verification of photogrammetry-based models. It introduces a verification framework that defines measurable data quality elements and establishes conditions to assess whether model quality is maintained, improved, or degraded. Validation through a university building case study demonstrates the framework’s ability to detect quality differences between visually similar models. Meeting only one of the three verification conditions, the new model shows quality degradation, primarily due to reduced positional accuracy and resolution, making it unsuitable to replace the previous version used in the Digital Twin. Additionally, the developed web tool prototype enables the automated calculation of the framework’s verification scores. This study contributes to the growing discussion on Digital Twin maintenance by providing practical insights for improving the reliability of geometric models.