Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems
Digital Twin (DT) technology represents the next evolution in a gradual shift from physical to digital models in civil engineering. Computer-Aided Drafting (CAD) revolutionized the industry by reducing the time and costs associated with documenting the design. Building Information Modeling (BIM) has since all but eliminated the need for physical design descriptors (i.e., drawings or physical models). A digital twin is a relevant abstraction of the physical asset. Itis most frequently used to model/improve/control manufacturing systems. Civil engineering applications of DT have been starting to emerge, but transportation infrastructure represents a challenging extension of DT technology because of its spatial scale and voluminous and time-varying data. However, DT is a powerful decision support tool for the design, maintenance, and management of transportation infrastructure, particularly for studying the interdependency with other infrastructure systems.
The term “digital twin” was coined by Michael Grieves in 2002 but the concept gained momentum from high-value product manufacturing industries such as automotive and aerospace (Grieves, 2014). Essentially, a DT model consists of three components; a physical environment (asset/product) that exists in the ‘real-world’, a virtual representation of that physical environment (asset/product) known as the digital model, and connections that feed data from the physical to virtual models and the flow of information back from the virtual to physical models (Grieves, 2014). Accurate digital 3D-models are needed to create a realistic and true DT to be used in a virtual environment. Often, the basis for these models (that replicate existing systems) is a reality capture approach like using LiDAR to create a 3D point cloud of the area of interest. Going beyond reality capture requires the addition of static data, meta-data, and other descriptive information. This data may be of different formats depending on the system being modeled. For example, detailed digital models for buildings can be created using standard Building Information Modeling (BIM) tools. BIM Levels of Development (LOD) defines the development stages in BIM systems: LOD 100 – Conceptual; LOD 200 – Approximate Geometry; LOD 300 – Precise Geometry; LOD 350 – Precise Geometry with Connections; LOD 400 – Fabrication-Ready Geometry; and LOD 500 – As-Built Models. For a transportation system, the static data would be the information associated with physical assets (i.e., road alignment or signage). The Digital Shadow is a digital model with an automated one-way data flow between the existing physical object and a digital object whereas the DT is the three-dimensional model with two-way data connections (Figure 1).
Interest in DT technology has greatly increased in the past few years in other industries due to advances in related technologies and initiatives such as Internet-of-Things, big data, multi-physical simulation, data management, and data processing. Further research is needed to better understand the process and realizing its true potential for large scale systems. Specifically, there is a lack of use- cases for transportation, and civil infrastructure in general, to validate and quantify the perceived benefits of DT technology against existing processes and systems.
The primary objective of this research is to explore the effectiveness of DT technology as a tool to understand interactions between transportation and other civil infrastructure systems. We will use the UTEP campus as a living lab by creating a DT model of campus. While the entire campus will be included in the reality capture model, it will vary in complexity, with a focus on a single building and the transportation network, at minimum. The DT will be approximated with a shadow model. We will either identify existing data sources on campus that can be utilized, supplemented with synthetic data to simulate the DT. Specifically, the research will:
- Summarize the existing literature around DT in transportation and provide an overview of the state of adoption;
- Create a baseline digital shadow of a selected infrastructure at the UTEP campus;
- Study impact of construction project schedule on the surrounding transportation infrastructure;
- Develop visualizations of the impact analysis.
|Principal Investigator||Jeffrey Weidner|
|Funding Source||The University of Texas at El Paso|
|Total Project Cost||$212,637|
|USDOT Award #||69A3551747124|
|Implementation of Research Outcomes||The concept of a DT is not well-established in the Research Project Description 2 transportation sector. This project will provide insights into the challenges and opportunities for DT implementation to study interactions between various civil infrastructure systems. The project will involve undergraduate and graduate students and integrate research findings into transportation courses to prepare the next generation of transportation professionals. A workshop will be organized to transfer the knowledge of DT to the stakeholders in the industry.|
|Impacts/Benefits of Implementation||The project has a potential outcome that could accelerate the adoption of DT technology that will bring positive impacts to the civil engineering industry.|