A BIM component-centred bridge digital twin for smart and practical bridge maintenance

For bridge visual inspection and maintenance, a fundamental task is to determine the condition of a bridge component from its appearance as captured by images. Then the information of the identified defects is recorded in documents and assessed collectively by bridge practitioners to determine what maintenance activities are required for the component. This engineering practice naturally fits for the idea of IFC components in BIM workflow. So, to automate this labour-intensive and time-consuming process, a smart and practical framework is proposed utilising BIM component-centred bridge digital twin system. In this system, sensors-equipped robotics is integrated with the bridge digital twin to identify any defect. Component-wise defect data is linked to the BIM model for detailed and holistic assessment, ensuring that maintenance decisions are fully informed. This framework is validated by demonstration of key functions including RTK-enabled drone for automatic defect localisation, defect quantification by computer vision, defect data storage in SQL and visualisation of enriched BIM model in interactive web-based platform for maintenance decision-making. It is demonstrated that the framework can streamline defect data transfer from on-site inspection to an online bridge digital twin, supporting decision-making processes by referencing relevant industrial standards.