Tom Bartley, director of infrastructure at Mind Foundry, discusses how developing technology will impact asset management
The transport revolution in the 1700s was spurred by the construction of the canal and rail networks within the UK. It meant that goods could be transported more efficiently and cheaply than ever before, and it was one of the core features of the Industrial Revolution. Historically, British bridges were simple wooden structures. However, the Industrial Revolution made cast and wrought iron popular materials for building more modern bridges. The 20th Century saw widespread adoption of reinforced concrete and prestressed concrete to ensure greater safety, durability, and performance.
The UK’s current 100,000 bridges and structures are simultaneously reaching an average age of over 50 years, which means these vital assets are entering a phase of deterioration. It has been estimated that clearing the full maintenance backlog on all bridges in the UK would cost approximately £5.44bn. However, without proactive intervention, this cost is expected to increase rapidly.
To address these challenges, innovative monitoring and asset management strategies are being increasingly adopted by asset owners. Techniques such as smart sensors or AI-enabled data analytics enable the real-time monitoring and assessment of a structure’s health, allowing asset owners to identify issues before they become critical.
Our civil infrastructure — including bridges, buildings, and roads — is crucial to the connectivity and mobility of our society and economy. These networks facilitate the movement of people, goods, and services, all of which have a direct impact on the UK’s productivity and quality of life. Therefore, without well-maintained infrastructure, economic activity can slow, and accessibility to communities diminish, highlighting its indispensable role in connectivity and mobility.
The need for robust asset management of ageing infrastructure
Despite the need for robust asset management to ensure the effective management of deteriorating infrastructure, the current maintenance processes are slow and labour-intensive. Asset managers, such as local governments and consultancies, have been relying on subjective condition ratings, photographs, and manual reports, all of which require specialists to inspect each site. This is not only a bureaucratic burden but also represents a significant financial burden, with asset owners spending millions of pounds annually on inspections alone.
Human error is also a factor, as subjective assessments such as rating the condition of a bridge on a 5-point scoring system can lead to inconsistent results. For example, If “1” means perfect and “5” means failure, then the human inspector reduces the vast majority of their damage assessments to vague guesses: 2, 3, or 4. What’s the difference between a high 2 and low 3? It’s impossible to know when looking at data like this.
This means that issues can go unnoticed or be misclassified, resulting in either an underestimate or an exaggeration, which can lead to potentially catastrophic consequences. Asset managers need a reliable way to monitor infrastructure and prioritise interventions, enabling them to act proactively rather than reactively and prevent failures before they happen. When discussing such critical infrastructure, these failures can be devastating. For example, the consequences of a collapsing bridge can include injuries and fatalities, reputational damage, restructuring and repair costs, social disruption and a disruption to local supply chains.
A retreat from traditional custodianship
A significant factor underlying the current infrastructure crisis is the shift in asset stewardship, which involves the responsibility of overseeing specific infrastructure assets. Historically, there was a strong sense of custodianship within the infrastructure sector, where asset managers would spend their entire careers overseeing a limited number of specific assets, allowing them to develop a deep understanding of these assets. This mitigated the risk of subjectivity, as it is easier to detect a change in a structure if the same person is looking at it yearly.
However, we’re now experiencing a demographic shift – many of these asset managers are approaching retirement. Simultaneously, asset owners are now using external consultants to fulfil the inspections. While these consultants bring a wealth of knowledge and expertise, the nature of their project-based work means that they don’t have the continuity associated with traditional custodianship.
This change in custodianship means that the data stored in asset management systems is often insufficient and fragmented, making it difficult to make comprehensive decisions about large-scale infrastructure estates. As a result, the critical insights gained from years of direct contact and experience with specific assets risks being lost, and the ability to track subtle changes over time in an asset’s condition are diminished. These changes underscore the pressing need for asset owners to consider deploying digital solutions to maintain asset integrity and ensure the long-term resilience of critical assets.
The case for digital custodianship
Asset owners are facing key challenges when it comes to proactively managing their assets: understanding their structures, making a compelling case for increased funding, and prioritising inspections and remedial work across thousands of assets. The solution to these challenges lies in digital custodianship. This shift aims to preserve institutional knowledge, integrate historical data, and leverage AI-driven insights to extend asset life cycles, thereby improving resilience and ensuring the correct information is available to the right people at the right time.
The implementation of AI agents is the cornerstone of digital custodianship. AI agents are systems where multiple intelligent agents interact within a shared environment to achieve collective goals and can deconstruct complex workflows into precise and scalable tasks. In this context, deploying AI agents can quantify, track and forecast asset condition and optimise maintenance costs across a manager’s portfolio. This may take different forms, such as gathering and processing data from historical and new inspections, or detecting, quantifying, and predicting deterioration using structural condition data and contextual information. With this assistance, the information can be processed and analysed to predict how the condition of an asset is likely to evolve.
Furthermore, digital custodianship can also provide engineers with a dynamic view of a defect’s evolution. This visual can provide engineers with access to what is happening to an asset, and crucially, the speed at which it’s deteriorating. This has the potential to usher in an era of more informed, data-driven asset management, moving towards continuous and predictive understanding.
The future of asset management
Beyond the core concept of digital custodianship, AI holds significant potential to revolutionise the process of inspection and examination, as it’s currently a highly physical and time-consuming process. The integration of AI into these original workflows will significantly accelerate and enhance the effectiveness of infrastructure monitoring. This increase in efficiency will lead to a reduction in the risks associated with infrastructure degradation.
AI can also mitigate the risk of subjective biases inherent in traditional monitoring methods. Instead of inspectors manually taking photographs of assets and then subjectively assessing the results, they use the images to do so. Through AI and new technologies, engineers can capture high-quality photos and accurately tag specific defects, locate issues using geo-referencing, and log detailed condition notes. The system could not only record its appearance today, but it would build an evolving digital memory that tracks its condition from day one. This would be beneficial because it would enable more effective prioritisation of maintenance tasks and mean that decisions are made based on evidence rather than subjective judgements.
Another element is the strategic deployment of sensors on structures. These can be installed on healthy structures to monitor for early indicators of deterioration continuously. Alternatively, if existing defects have been found, specialised sensors can be placed on the defect itself. These sensors provide asset managers with real-time information on how a defect is forming, its rate of progression and any critical changes. The amount of sensor data collected can then be used to train advanced AI models, which can learn from the data to forecast how defects will evolve over time and whether they require intervention. This allows for highly effective and targeted management.
However, to ensure we can reap the benefits of digital custodianship, it relies on one critical element: better data. The efficacy of advanced AI and ML algorithms is directly reliant on the accuracy, completeness and consistency of the data they process. Insufficient data can lead to diminished model performance, resulting in unreliable insights and, consequently, flawed decision-making. In a sector where the stakes are so high, asset owners must prioritise building comprehensive data governance frameworks and high-quality data management as a strategic imperative, as it directly underpins the ability to extract actionable intelligence and drive innovation.
The project with ARIA: Advancing AI for critical infrastructure
The Advanced Research and Invention Agency (ARIA) is the UK government’s research and development funding agency, established by an Act of Parliament and sponsored by the Department for Science, Innovation and Technology (DSIT). Its mission is to “unlock technological breakthroughs that benefit everyone” by funding teams of scientists and engineers to “pursue research at the edge of what is scientifically and technologically possible”.
The project is part of ARIA’s £59m Safeguard AI Programme, which aims to “establish a new standard for AI safety”, enabling society to harness the full economic and social benefits of AI while minimising associated risks.
Mind Foundry, in conjunction with WSP, a leading global engineering consultancy, is among the nine research teams funded to explore how AI systems can unlock transformative improvements in the management of the UK’s critical infrastructure. Their specific focus is on ensuring resilient and proactive infrastructure management through the use of AI-driven tools that are both cost-effective and sustainable.
Across the UK, many of the bridges, tunnels, roads and flood defences that underpin our society are deteriorating. The goal of the project is to demonstrate how safeguarded AI can significantly enhance the management of these assets.
When dealing with critical infrastructure, such as bridges and railways, even 99.9% accuracy is not sufficient, as real lives are at stake. This is why Mind Foundry and WSP’s work with ARIA is so essential. Their funding supports the development of verified AI safeguards, which ensure system recommendations can be trusted completely and without compromise.
Instead of relying on traditional inspection cycles and slow interventions, the Mind Foundry and WSP teams are committing to building intelligent, proactive systems that can anticipate faults and problems before they escalate. This innovative approach has the potential to reduce costs and minimise disruption, which will ultimately improve public safety. This new process for management enables infrastructure to be consistently monitored and safely managed at scale.
Extending the lifespan of the UK’s critical infrastructure
It’s crucial to emphasise that the integration of AI into infrastructure management is not intended to replace human decision-makers. The engineers and asset managers possess invaluable expertise that AI, on its own, cannot replicate. The true power of implementing AI in infrastructure lies in combining irreplaceable human knowledge with the unparalleled data processing and analytical capabilities of AI.
Furthermore, the collaboration between AI and human professionals fosters a culture of improvement and innovation. AI can identify trends and patterns that even the most well-trained experts cannot spot, enabling them to make better-informed decisions and facilitate long-term planning for maintenance and resource allocation.
Similarly, digital custodianship is not intended to replace existing systems of asset management; many functions are still served well by them. This adds an extra layer of intelligence on top. By integrating AI into every stage of the asset management lifecycle — from data collection and analysis to predictive modelling and decision support — we can revolutionise how our critical and ageing infrastructure is managed. Digital custodianship promises not only enhanced efficiency and reduced costs, but more importantly, the safety and resilience of the UK’s built assets for future generations.
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