In recent discussions on digital twins (DT) in construction and infrastructure, a multitude of terms and concepts have emerged, such as digital models, digital shadows, twin systems, and digital threads. These distinctions are rooted in theoretical frameworks and the evolving definitions of digital twins.

The situation mirrors the development of Building Information Modeling (BIM), where various dimensions and maturity levels—from 2D to 7D and beyond—are all considered as BIM, albeit with different levels of sophistication. So, if one is curious to know how BIM is used in a project, first they must know to what extent, level, and maturity it is used.

Digital twins can be categorized into several maturity levels: descriptive, informative, predictive, comprehensive, and autonomous [1]. At the highest maturity levels, DTs leverage artificial intelligence (AI) and machine learning (ML) to conduct predictions, simulate "what-if" scenarios, and provide actionable insights for decision-makers. These advanced stages can even lead to the full or semi-automation of decision-making processes.

However, achieving such advanced levels of DT maturity is not always necessary or feasible for every project. The adoption of a digital twin should align with the specific needs and readiness of the project, including business requirements, institutional capacities, technical capabilities, and cultural readiness. It is crucial to recognize that adopting the highest maturity level of DT is recommended only for projects that are well-prepared and have a genuine need for such advanced capabilities, rather than merely a desire [2].

To ensure successful DT implementation, it is imperative to employ frameworks or guides like the capability periodic table developed by the Digital Twin Consortium [3]. This approach helps align the project's needs with its envisioned goals, and to some extent, facilitates the development of a practical and sustainable roadmap for transitioning from the current state to the desired level of DT maturity. Without a well-defined strategy, projects may encounter significant threats that could become unmanageable during the construction phase.

Neglecting the essential components of data, technology, business considerations, and the application of DT during the front-end planning stages can lead to numerous challenges and threats (negative risks). Projects that fail to address these elements early on are likely to encounter difficulties that compromise their success, similar to issues seen in various construction and industrial projects.

In summary, while digital twins offer promising advancements in infrastructure and construction projects, their successful adoption requires a strategic approach and careful evaluation of project needs and the required maturity level accordingly. Ensuring that the project’s readiness and requirements are aligned with the chosen level of DT implementation is crucial for achieving desired outcomes and mitigating potential risks.

References

• Seaton, H., Savian, C., Sepasgozar, S., & Sawhney, A. (2022). Digital twins from design to handover of constructed assets. Royal institute of chartered surveyors, London.
• Mahdiyar, A. (2024, August 23). Decision Support Tool for Determining the Optimum Digital Twin Maturity Level in Construction Projects. The Chartered Institute of Building.

https://www.ciob.org/blog/scholarship-win-supports-the-selection-of-digital-twin-tools

• Digital Twin Consortium. (2024). Digital Twin Capabilities Periodic Table. https://www.digitaltwinconsortium.org/initiatives/capabilities-periodic-table/

https://www.ciob.org/