Coupling physical and digital built environments for proactive asset management

Various sectors of the built environment (BE) are threatened by deterioration processes that have an increasing trend due to ageing infrastructure, current extreme climatic conditions, increasing urban population, and limited financial resources [1]. Digitalization has the potential to transform the current processes of managing and sharing critical information that can enhance decision-making and, in the long term, enable efficient and sustainable BE. However, despite the recent technological advancements, BE, and particularly critical infrastructure systems are still managed following a traditional approach in both technological but also organizational, and institutional aspects. As a result, they do not take full advantage of the recent technological developments that can enable a more sophisticated approach that involves the incorporation of real-time data streams and the employment of advanced analytical methods for efficient management of resources and risks. To overcome this challenge, the utilization of technologies and advancements provided by Civil Infrastructure 4.0 (CI4.0) [2] accelerate the digitalization of the BE focusing on critical infrastructure systems.

 

This study focuses on providing an overview of the pillars for the next generation BE, which aims to enable an interconnected and collaborative ecosystem across cities, infrastructure, and societies. Various case studies are presented, including large residential regions, transportation networks across waterways, and buildings in which digitalization can play a pivotal role in providing instantly information to the BE stakeholders for enhanced decision-making. These are based on obtaining real-time data from the surrounding environment to assist in predicting the current and future states of BE. For example, obtained information derived from advanced microcontrollers measure the deteriorating performance of the ageing infrastructure systems over waterways and the flood levels in real-time. At the same time, datasets are incorporated into a high-performance machine hosted in cloud and deep-learning algorithms to predict the upcoming states of the infrastructure and climatic risk. In the case of an emergency state (e.g., river overflow, flash floods, or infrastructure disruption), the management system generates an alarm. At the same time, the models also predict infrastructure deterioration to inform critical stakeholders promptly to take action and adapt the societal functions accordingly. Digitalization is expected to enable a flourishing society and physical and natural environment across our cities and infrastructure, which play a significant role in the upcoming Society 5.0.

References

[1] Pytharouli, S., Michalis, P. and Raftopoulos, S. (2019). From Theory to Field Evidence: Observations on the Evolution of the Settlements of an Earthfill Dam, over Long Time Scales. Infrastructures 2019, 4, 65. https://doi.org/10.3390/infrastructures4040065

[2] Michalis, P., Konstantinidis, F. and Valyrakis, M. (2019). The road towards Civil Infrastructure 4.0 for proactive asset management of critical infrastructure systems. Proceedings of the 2nd International Conference on Natural Hazards & Infrastructure (ICONHIC), 23–26 June, Chania, Greece, pp. 1-9.