Author: Professor Ravi Shankar Srinivasan, Associate Dean for Research and Strategic Initiatives, University of Florida
Professor Ravi Shankar Srinivasan, Associate Dean for Research and Strategic Initiatives, University of Florida, introduces our Smart Cities and Ageing Infrastructure mission by exploring the transformative potential of smart cities.
He illustrates how advanced technologies like the Internet of Things and Digital Twins can help manage both new and ageing infrastructure, improving urban sustainability and resilience.
Smart cities are the talk of the day.
I offer this statement with some caution, considering the various definitions of smart cities that can be found online. Even though there may be different interpretations of smart cities, the growing adoption of technologies like the Internet of Things, mobile/edge computing, and sensor networks is undoubtedly making cities smarter. This was echoed by John Stankovic's observation in his foreword in the 'Smart Cities: Foundations, Principles, and Applications' book that I co-edited where he noted that smart cities are all about increasing the acceleration of 'smartness' in cities.
While several definitions exist, from a researcher-teacher-professional in the Architecture-Engineering-Construction-Operation (AECO) domain, I define smart cities as those that embody the reliable and adaptive ‘fusion’ of spatio-temporal data to integrate new and ageing built infrastructure and urban landscapes for a sustainable future.
Figure 1 below illustrates this smart city definition which is the convergence of spatial and temporal data from the built environment and urban landscapes, along with the ongoing flow of data for visualisation and exploration.
Figure 1. Smart Cities in the AECO domain
1. Song, H., Srinivasan, R.S., Sookoor, T., Jeschke, S. (eds). Smart Cities: Foundations, Principles, and Applications. Wiley & Sons, 2017.
To elaborate, the spatial dimension (X-axis) of a smart city extends from individual spaces to entire urban landscapes. Meanwhile, the temporal dimension (Y-axis) spans the entire lifecycle of the built environment, from design and construction to operation and demolition. The Z-axis of this smart city convergence is the data stream that extends from visualisation and exploration, data sensing, data acquisition, data transformation/cleansing/preprocessing, segmentation/feature extraction, to data estimation/prediction. This smart cities’ definition aligns with at least five UN Sustainable Development Goals (SDG):
SDG6: Clean Water and Sanitation
SDG 7: Affordable and Clean Energy
SDG 9: Industry, Innovation, and Infrastructure
SDG 11: Sustainable Cities and Communities
SDG 13: Climate Action
Leveraging smart systems
One significant accomplishment of smart cities is that the concept brought together all disciplines – from social scientists to structural engineers, from data scientists to urban planners – united by a common goal: to elevate the smartness of interconnected components, systems, and system-of-systems.
While the design, engineering, construction, and operation of newly built infrastructure have seen notable strides toward sustainability and resilience, globally, ageing infrastructure continues to pose a formidable challenge in the face of increasing demands and the escalating impacts of climate change.
In the UK, the Royal Academy of Engineering is actively addressing the challenges of ageing infrastructure through a combination of proactive measures and innovative solutions. By leveraging a smart city suite of system-of-systems, which includes advanced technologies for monitoring, maintenance, and upgrades, the UK could effectively manage its ageing infrastructure and ensure its long-term resiliency and sustainability.
Digital Twins as a smart city solution
Considering the diverse range of technologies and expertise involved in the smart cities’ paradigm, it's worth discussing Digital Twin as an assuring smart cities’ solution for ageing infrastructure within the AECO domain. Digital Twins are virtual replicas of physical systems that could be utilised to collaborate, manage, maintain, and more importantly, conduct simulations such as what-if scenarios? I apply the same reservations when I define Digital Twins, here as well for similar reasons as stated above. In any case, Digital Twins play a major role in the design-engineering-construction-operation-demolition continuum, that is, the entire life cycle of the intelligent built infrastructure and urban landscapes that form the fabric of smart cities.
Take the Digital Twin solution in the operation phase of a building for example. In general, as data is consistently acquired using sensing systems from individual rooms, automated pre-processing, transformation, segmentation, and feature extraction could be completed for maintenance and prediction of what-if? scenarios.
To further explore this idea, let us say, these sensing systems are situated in the air-handling unit rooms of an ageing-built infrastructure. These sensing systems could very well extend beyond typical airflow, temperature sensors, and include other non-traditional sensing technologies such as high-fidelity microphones listening to the acoustics captured from different locations of the ventilation systems and vibration sensors too. As we gather data, the built-in algorithms could extract features and develop what-if? scenarios such as mechanical system fault detections in the near-term and long-term prediction of mechanical system failures that considerably affect facility management schedule and operating budget of the built infrastructure , all the while visually enabling experts to alter model parameters for in-depth testing and implementation.
There is no doubt, that while these innovative solutions offer opportunities, they do provide challenges owing to embedded devices and the enormous amount of data that is gathered and transferred. For one, privacy considerations and data transfer are challenging. While privacy skirts the ethical issues, the data (in)security during transfer relates to cybersecurity and related issues. Nevertheless, the contribution of Digital Twins, as a smart cities’ solution, in the world of ageing infrastructure cannot be understated.
Digital Twins at the University of Florida
At the University of Florida, a team of researchers have embarked on a project to digitally twin all the buildings on campus, mostly existing ones, with a goal of achieving a smart campus, one-building-at-a-time. Among others, new innovative connectors are under development to seamlessly conduct what-if? scenarios, see figure 2 below. Like Digital Twins, there are numerous smart cities solutions that could be expertly paired to monitor, maintain, and upgrade the ageing infrastructure to ensure long-term resiliency and sustainability.
2. Our preliminary attempt using acoustic signatures to predict mechanical failures is available in, Srinivasan, R.S., Islam, M.T., Islam, B., Wang, Z., Sookoor, T., Gnawali, Om., Nirjon, S. Preventive Maintenance of Centralised HVAC Systems: Use of Acoustics Sensors, Feature Extraction, and Unsupervised Learning. In Proceedings of Building Simulation 2017 (supported by National Science Foundation award #1619955)
Figure 2. This diagram focuses on DT-enabled energy optimisation using a custom connector for bi-directional data flow between NVIDIA’s Omniverse DT and EnergyPlus™.
Image credit: Deepak Balakrishnan, University of Florida.
Share your insights
With this background, I encourage all the researchers, policymakers, practitioners, and organisations working in the area of built infrastructure to share your latest research, to help advance this smart cities’ mission to achieve global resiliency and sustainability, one building-at-a-time.
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