Leveraging Digital-Physical Integration for Enhanced Infrastructure Management

The built environment (BE) across various sectors faces significant challenges due to increasing deterioration, ageing infrastructure, extreme climatic conditions, rising urban populations, and limited financial resources [1]. Digital transformation offers the potential to revolutionize current practices for managing and sharing key information, improving decision-making processes and enabling more efficient and sustainable BE in the long term. However, despite recent advancements in technology, critical infrastructure systems within the BE continue to rely on traditional management approaches in terms of technology, organizational structure, and institutional frameworks. Consequently, they fail to fully leverage emerging technologies that could enable advanced resource and risk management through real-time data integration and enahnced analytical methods.

Adopting technologies associated with Infrastructure 4.0 (CI4.0) [2] can accelerate the digitalization of BE, with a particular focus on infrastructure systems. This study highlights the foundational elements of a next-generation BE designed to foster an interconnected and collaborative ecosystem focused on cities, infrastructure, and societies. Several case studies are explored, including large residential developments, transportation networks, and buildings, demonstrating the transformative potential of digitalization in delivering real-time information to stakeholders, thereby enhancing decision-making processes.

These efforts rely on the acquisition of real-time data from the environment to predict both current and future conditions of the BE. For instance, advanced microcontrollers are utilized to monitor the declining performance of ageing infrastructure over waterways and to measure flood levels in real-time. Datasets are processed on high-performance cloud-based systems, utilizing deep learning algorithms to forecast infrastructure conditions and climatic risks. In emergency scenarios, such as river overflows, flash floods, or infrastructure failures, the system generates timely alerts. Moreover, predictive models provide early warnings about infrastructure deterioration, enabling critical stakeholders to respond proactively and adapt societal operations accordingly.

References

[1] Michalis, P., Vintzileou, E. (2022). The Growing Infrastructure Crisis: The Challenge of Scour Risk Assessment and the Development of a New Sensing System. Infrastructures, 7(5), 68. https://doi.org/10.3390/infrastructures7050068

[2] Xu, Y., AlObaidi, K., Michalis, P. and Valyrakis, M. (2020). Monitoring the potential for bridge protections destabilization, using instrumented particles. Proceedings of the International Conference on Fluvial Hydraulics River Flow, Delft, The Netherlands, 7–10 July 2020; pp. 1-8. eBook ISBN 9781003110958.