Much attention has been focused in recent years on our national infrastructure, particularly following passage of the $1 trillion Bipartisan Infrastructure Law in 2021. Much of our current infrastructure was built several decades ago and now shows signs of severe wear and degradation. Inevitably, this attention rises to the fore whenever there is a well-publicized failure: a bridge collapses, a pipe bursts, or a highway is closed for emergency repair. And this state of affairs is by no means limited to the United States. Major infrastructure improvements are taking place globally, and AI-optimized maintenance is playing a vital role.
The challenge of infrastructure decay and upgrading is particularly acute in urban centers. According to the World Bank, by 2050, more than two-thirds of the world’s population will live in cities. The challenges of safely and efficiently operating a major urban center include accessibility, mobility, sustainability, efficiency, and overall quality of life.
Whether energy, water, sewage, transport, or public safety, AI provides a wide range of capabilities that can enhance the operating efficiency of current infrastructure.
Optimize infrastructure maintenance with AI
Day-to-day management of a large urban center is fraught with countless operational challenges in areas as diverse as energy, drinking water, mobility, safety/crime, food supplies, waste management, and infrastructure operation. The challenges associated with installing and maintaining these expensive and long-lived assets often overlap, demanding innovative solutions that balance many simultaneous factors, most notably cost, both capital and recurring.
For example, AI applied to spatial object recognition from satellite imagery, in combination with machine learning for route optimization, can improve infrastructure planning in the limited space that cities have available, supporting and prioritizing work while aligning construction and maintenance demands with limited and costly resources.
Large municipalities around the globe have employed AI-optimized infrastructure maintenance in creative and effective ways to address their urban infrastructure challenges:
- Seoul, South Korea has built an integrated public transport system that uses smart cameras in subways to gather information on passenger volumes, using the data to modify the speed and frequency of trains in real-time and improve rider safety.
- In Helsinki, Finland, Ramboll Group has developed machine learning algorithms that use AI-based time-series forecasting to predict the quality of water being provided by water utilities. Quality is analyzed in the context of existing and proposed environmental regulations, enabling the city to monitor treatment processes continuously. This shifts the focus from troubleshooting to predictive risk assessment and dynamic facility optimization.
- Rome and other large Italian cities suffer water grid leakage of over 40% annually, an extraordinary challenge given the city’s increasing water demand and recent droughts. AI applications help tackle this challenge by proactively monitoring, detecting, and preventing leakage.
Maintain infrastructure with AI-enabled digital twins and centralized control
An emerging trend in urban infrastructure management is digital platforms, sometimes known as urban data platforms (UDPs). Such platforms monitor and control all of a city’s operational activity, providing a holistic view and allowing for event correlation, fast and effective root-cause analysis, predictive/prescriptive recommendations, automated incident management, and operational insights through visualization.
Major cities, including Dublin and Singapore, have created digital twins—dynamic digital replicas of each city’s physical assets and environments and their interdependencies—for urban planning purposes, using machine learning to predict future events and trends. A digital twin can support day-to-day operations, simulate a natural disaster and its potential impact on the city, or evaluate the flow of air that cools a city and the trees that provide shade for streets and parks. With the evolution of new AI-optimized technologies coupled with massive data availability and ever-growing computer processing speeds, digital twins will become increasingly valuable enablers of data-driven decisions. A recent blog by Landvault highlights the role of digital-twins for predictive maintenance and risk assessment and mitigation in urban environments.
Artificial Intelligence is a powerful tool for driving sustainable transition to a more resource-efficient, livable, and citizen-focused urban environment. Applied to infrastructure management and planning, AI can maximize the value of data provided by existing infrastructure (e.g. traffic controllers, sensors, video data, etc.), fleet data, public transport, and even third-party data. The opportunity is vast; the tools and solutions exist today.
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