Digital twin platform based on an identity-semantic-interaction framework to improve bridge management workflows

Digital technologies have been introduced into infrastructure asset management practices since 1990s. Current digital frameworks are less flexible in large-scale collaborative environments and have limited analytical capability. To address this challenge, this study develops a digital twin platform based on an identity-semantic-interaction framework. It integrates Bridge Information Modeling (BrIM) by applying Building Information Modeling (BIM) principles, along with Internet of Things (IoT) data, and semantic modeling protocols to align multi-source data through a unified property and identifier system, which enables continuous synchronization between the physical bridge and its virtual counterpart. By utilizing capabilities such as parametric modeling, IFC-based interoperability, and cloud-enabled digital-twin coordination, the proposed platform demonstrates a streamlined yet semantically rich digital twin in the bridge engineering domain. The associated workflow monitors in real time and manages inspection records during the entire bridge life cycle and enhances data traceability. Even though limitations remain in scalability, automation, and advanced analytics, the current research significantly lowers the technical barrier to digital twin adoption in bridge projects by eliminating manual IFC data exchange, maintaining stable component identities through PCIDs, and enabling reusable semantic templates that simplify integration of BIM, inspection, and IoT data. It facilitates practical digital twin development and possible future extensions in adaptive modeling, data analytics, and multi-stakeholder collaboration for a proactive asset management.