Digital Twins | Stakeholder Engagement | Infrastructure Engineering

I am currently involved in leading the stakeholder engagement communications plan for the renovation of one of the world's longest pedestrian bridges located in Chattanooga, Tennessee - the Walnut Street Bridge ~ originally built circa 1891. As I work on this project to inform the various types of stakeholders affected by this needed renovation, I began to think about digital twin technology. I came across a great article by IBM that I encourage you to read if you want to know more.

In the ever-evolving landscape of infrastructure development, the introduction of digital twin technology marks a revolutionary stride forward. This cutting-edge innovation offers a dynamic digital replica of our physical construction projects, promising not only to enhance the accuracy and efficiency of our designs but also to pave the way for proactive problem-solving and optimization throughout the project's lifecycle. As we delve deeper into the potential of this technology, we stand on the brink of a transformative era in construction, where our decisions are more informed, our processes streamlined, and our outcomes vastly improved, ensuring that our projects deliver maximum value to all stakeholders involved.

Digital twins have risen as a potent instrument in the infrastructure engineering industry. A digital twin is a digital replica of a physical entity, system, or process. In the context of infrastructure engineering, it can represent bridges, tunnels, buildings, roads, water systems, electrical grids, and more. From the bit of reading on the subject, here is how our industry benefits from incorporating this technology:

Design and Planning: Simulation and Analysis: Engineers can use digital twins to simulate various scenarios, like traffic flow on a proposed road or the structural behavior of a bridge under specific load conditions

• .Optimization: By simulating different design alternatives, engineers can identify optimal solutions in terms of cost, performance, and resilience.

Construction: Real-time Monitoring: As infrastructure is being built, sensors can feed real-time data into the digital twin to monitor construction progress, track materials, and ensure quality control.

• Resource Management: Digital twins can help optimize the allocation of resources such as machinery, materials, and workforce.

Operation and Maintenance: Predictive Maintenance: By continuously updating the digital twin with real-time data from sensors, it's possible to predict when a component might fail or require maintenance, thus avoiding costly breakdowns.

• Performance Monitoring: Digital twins allow operators to monitor the performance of infrastructure systems in real time, ensuring they function optimally and efficiently.
• Scenario Analysis: By simulating different scenarios on the digital twin, such as increased traffic load or changing environmental conditions, operators can plan and prepare for changes.

Asset Management: Lifecycle Analysis: Digital twins provide insights into the entire lifecycle of infrastructure assets, from construction to decommissioning.

• Value Assessment: It becomes easier to assess the value of infrastructure assets, considering factors like wear and tear, usage, and anticipated future performance.

Disaster Preparedness: Simulation: Digital twins can simulate the impacts of natural disasters, such as floods, earthquakes, or hurricanes, allowing for better preparedness and response.

Resilience Planning: Infrastructure systems can be designed and upgraded based on insights from the digital twin to be more resilient to potential threats.

Integration with Other Systems: Digital twins can be integrated with digital systems like GIS (Geographical Information Systems), traffic management systems, and energy grids to create a holistic view of the urban or regional infrastructure landscape.

Training and Capacity Building: Engineers, operators, and maintenance personnel can be trained using digital twins, allowing them to familiarize themselves with systems and processes in a risk-free virtual environment.

And finally - Stakeholder Engagement: Communications professionals can use visualizations from digital twins to engage stakeholders, from the public to policymakers, providing a clear and interactive representation of infrastructure projects.

As sensor technology, IoT (Internet of Things), cloud computing, and artificial intelligence continue to advance, the capabilities and applications of digital twins in the infrastructure engineering industry will expand, leading to more efficient, resilient, and sustainable infrastructure systems.

Below are references I encourage you to use if you want to know more about this powerful technology. I am intrigued and looking forward to seeing how our industry will continue to use this tool.

References

Alhartomi, M. A., Salh, A., Audah, L., Alzahrani, S., Alzahmi, A., Altimania, M. R., Alotaibi, A., Alsulami, R., & Al-Hartomy, O. (2023). Sustainable resource allocation and reduce latency based on federated-learning-enabled digital twin in IoT devices. Sensors (14248220), 23(16), 7262. https://10.3390/s23167262

Barker, M. (2023). Artificial intelligence-based internet of manufacturing things systems, digital twin data modeling and visualization tools, and multi-sensory extended reality and geospatial mapping technologies in the immersive industrial metaverse. Economics, Management & Financial Markets, 18(1), 41-56. https://10.22381/emfm18120233

Bofill, J., Abisado, M., Villaverde, J., & Sampedro, G. A. (2023). Exploring digital twin-based fault monitoring: Challenges and opportunities. Sensors (14248220), 23(16), 7087. https://10.3390/s23167087

Bouchabou, D., Grosset, J., Nguyen, S. M., Lohr, C., & Puig, X. (2023). A smart home digital twin to support the recognition of activities of daily living. Sensors (14248220), 23(17), 7586. https://10.3390/s23177586

Bunjaridh, Y., Rahman, R. A., & Yusof, L. M. (2023). Digital twin production in the architecture, engineering, construction and operation industry: Organizational attributes and strategies. Journal of Engineering, Project & Production Management, 13(3), 1-10. https://10.32738/JEPPM-2023-0019

Chalal, L., Saadane, A., & Rachid, A. (2023). Unified environment for real time control of hybrid energy system using digital twin and IoT approach. Sensors (14248220), 23(12), 5646. https://10.3390/s23125646

Chauhan, H. S., Babbar, H., & Rani, S. (2023). D2PG: Deep deterministic policy gradient-based vehicular edge caching scheme for digital twin-based vehicular networks. International Journal of Performability Engineering, 19(5), 350-358. https://10.23940/ijpe.23.05.p7.350358

Dong, X., Huang, J., Luo, N., Hu, W., & Lei, Z. (2023). Design and implementation of digital twin diesel generator systems. Energies (19961073), 16(18), 6422. https://10.3390/en16186422

Drobnyi, V., Hu, Z., Fathy, Y., & Brilakis, I. (2023). Construction and maintenance of building geometric digital twins: State of the art review. Sensors (14248220), 23(9), 4382. https://10.3390/s23094382

Gourisetti, S. N. G., Bhadra, S., Sebastian-Cardenas, D., Touhiduzzaman, M., & Ahmed, O. (2023). A theoretical open architecture framework and technology stack for digital twins in energy sector applications. Energies (19961073), 16(13), 4853. https://10.3390/en16134853

Henley, S. (2023). Extended reality and cognitive digital twin technologies, 3D space mapping and image processing computational algorithms, and predictive geospatial modeling and simulation tools in the industrial metaverse. Economics, Management & Financial Markets, 18(1), 25-40. https://10.22381/emfm18120232

Jasiński, M., ?aziński, P., & Piotrowski, D. (2023). The concept of creating digital twins of bridges using load tests. Sensors (14248220), 23(17), 7349. https://10.3390/s23177349

Jiao, Z., Du, X., Liu, Z., Liu, L., Sun, Z., & Shi, G. (2023). Sustainable operation and maintenance modeling and application of building infrastructures combined with digital twin framework. Sensors (14248220), 23(9), 4182. https://10.3390/s23094182

Kajba, M., Jereb, B., & Cvahte Ojster?ek, T. (2023). Exploring digital twins in the transport and energy fields: A bibliometrics and literature review approach. Energies (19961073), 16(9), 3922. https://10.3390/en16093922

Kumari, N., Sharma, A., Tran, B., Chilamkurti, N., & Alahakoon, D. (2023). A comprehensive review of digital twin technology for grid-connected microgrid systems: State of the art, potential and challenges faced. Energies (19961073), 16(14), 5525. https://10.3390/en16145525

Litavniece, L., Kodors, S., Adamoniene, R. ū, & Kijasko, J. (2023). Digital twin: An approach to enhancing tourism competitiveness. Worldwide Hospitality and Tourism Themes, 15(5), 538-548. https://10.1108/WHATT-06-2023-0074

Ma, Y., Zhu, X., Lu, J., Yang, P., & Sun, J. (2023). Construction of data-driven performance digital twin for a real-world gas turbine anomaly detection considering uncertainty. MDPI. https://10.3390/s23156660

Riaz, K., McAfee, M., & Gharbia, S. S. (2023). Management of climate resilience: Exploring the potential of digital twin technology, 3D city modelling, and early warning systems. Sensors (14248220), 23(5), 2659. https://10.3390/s23052659

Rjabt?ikov, V., Rass?lkin, A., Kudelina, K., Kallaste, A., & Vaimann, T. (2023). Review of electric vehicle testing procedures for digital twin development: A comprehensive analysis. Energies (19961073), 16(19), 6952. https://10.3390/en16196952

Schmidt, C., Volz, F., Stojanovic, L., & Sutschet, G. (2023). Increasing interoperability between digital twin standards and specifications: Transformation of DTDL to AAS. Sensors (14248220), 23(18), 7742. https://10.3390/s23187742

Volz, F., Sutschet, G., Stojanovic, L., & Usl?nder, T. (2023). On the role of digital twins in data spaces. Sensors (14248220), 23(17), 7601. https://10.3390/s23177601

Wu, H., Ji, P., Ma, H., & Xing, L. (2023). A comprehensive review of digital twin from the perspective of total process: Data, models, networks and applications. Sensors (14248220), 23(19), 8306. https://10.3390/s23198306

Yal?in, T., Paradell Solà, P., Stefanidou-Voziki, P., Domínguez-García, J. L., & Demirdelen, T. (2023). Exploiting digitalization of solar PV plants using machine learning: Digital twin concept for operation. Energies (19961073), 16(13), 5044. https://10.3390/en16135044