Bridge Digital Twins: Fusion of Intelligent Transportation Systems (ITS) Sensor Data and Bridge Information Modeling (BrIM) for Interoperability

Transportation infrastructure is a critical component to the economy, security, and wellbeing of every country. Aging and deterioration are the two primary reasons for damages to the infrastructure, mainly bridges, and the increase in the heavy live loads is raising even more concerns. The limited amount of available funds for the repair and preservation of bridges adds another layer of complexity to this problem. Therefore, there is an immediate need to develop new methodologies, technologies, and techniques to improve the utilization of available data and their integration. Data play a significant role in the lifecycle management of bridges. However, the current practice of managing infrastructure involves the use of an abundance of data produced by a variety of non-interoperable information systems. Thus, the lack of interoperability creates major challenges in deployment of a fully integrated management system for the infrastructure. This research focuses on the development of a Digital Twin that could be used for integrating the live load traffic data with bridge lifecycle data to mirror the bridge behavior and offer an integrated bridge management system. This approach can assist the lifecycle management of bridges through the optimized use of the integrated data. For creating the digital twin, the live load data captured by Intelligent Transportation Systems (ITS) has been integrated with a Bridge Information Model (BrIM). Even though many ITS sensors could be integrated into the system, this dissertation focused on the weight sensor data collected by Weigh in Motion (WIM) ITS systems as its scope and integrated them into the BrIM model. A mock-up bridge was built as a physical model, and Arduino systems were embedded into it to replicate WIM systems. The IfcSensor entities have been utilized to prepare the required IFC entities to represent the WIM sensors. The streamed data were then integrated into a BrIM model through neutral data exchange schemas and scripting. Through this approach, stove-pipe boundaries have been removed, sensor data could be streamed in the Digital Twin of bridge, and the level of data utilization by different stakeholders have been improved. This method has pressing applications for the bridge management systems (BMS) in practice. The augmentation of a real-time stream of traffic data into the BrIM model will help the operating entities to detect the overweight vehicles in an immersive environment and make early actions against their passage over critical bridges. These vehicles could be detected, stopped, or enforced before touching the bridge. Engineers and designers can augment the live load effects into their structural analysis in BrIM to evaluate the Structural Health Monitoring (SHM) data along with the live loads and offer sharpened designs for bridge rehabilitation and repairs. The BrIM data could be mapped into GIS, and planners can evaluate the bridges at a network level and have an advanced perception and decision making about allocating the funds and resources according to the need, priorities, and necessities.