The state of the United Kingdom’s infrastructure, particularly its bridges, is approaching a critical juncture. Most of the nation’s modern built assets date back to a concentrated period between the 1950s and 1970s. Now, as these structures age, they face accelerated deterioration, putting their structural integrity at increasing risk. Current data indicates that approximately one in every 24 bridges in the UK is deemed “substandard,” with nearly three thousand of the busiest bridges classified as being in “poor” condition. This situation is compounded by the sheer volume of infrastructure requiring upkeep, which makes regular and precise monitoring essential to forestall failures and maintain operational safety.
The challenge in addressing these maintenance demands is intensified by the prevailing management systems, which remain heavily reliant on outdated methods. Inspections are traditionally manual, involving physical site visits by specialists who document findings through photos, paper forms, and subjective condition ratings. Such processes are labour-intensive and costly, with asset owners collectively spending millions annually just on inspections. These assessments are fraught with inconsistency and subjectivity, as defect severity and condition scores can vary widely between inspectors. Moreover, access limitations necessitate road closures, further complicating and prolonging inspection times.
Another pivotal concern is the erosion of custodianship in asset management. Historically, infrastructure assets were overseen by long-serving managers who developed deep, nuanced understandings of those specific structures over time. Their continued involvement allowed them to detect subtle changes and trends, mitigating the risk introduced by subjective inspection methods. However, a wave of retirements has led to reliance on external consultants unfamiliar with the historical context, resulting in fragmented and less-informed decision-making. The accumulated data in current management systems falls short of providing cohesive insights necessary for effective prioritisation and maintenance planning.
Digital Custodianship emerges as a compelling solution to these problems by blending structured data collection, artificial intelligence (AI), and human expertise into a unified framework. With AI processing historical records augmented by machine learning, asset managers can now predict future deterioration trajectories of infrastructure components. For example, computer vision technology can accurately measure and track defects like cracks over time by overlaying new images onto historical photos, granting engineers a dynamic and quantifiable understanding of degradation rates. This restores the continuity of knowledge and insight that traditional custodianship once ensured.
Beyond digital repositories, AI-driven technologies hold promise for revolutionising inspection processes themselves. Imagine engineers using simple handheld devices to capture high-quality geotagged images, annotate defects, and log observations in real time through a streamlined and synchronised platform. This approach not only cuts inspection times and costs but also builds a comprehensive, longitudinal digital memory of an asset’s condition from the outset—eliminating surprises and enabling evidence-based prioritisation of repairs.
Complementing visual inspections, sensor technologies are being deployed as well. These sensors either monitor intact structures continuously or focus on known defects, supplying real-time data on structural changes. When integrated with AI models, this sensor data enhances the ability to forecast deterioration and schedule interventions before costly failures occur, further strengthening the digital custodian role.
The urgency of these innovations is underscored by broader infrastructure reports over the last decade that highlight a growing maintenance backlog and escalating repair costs. For instance, a 2019 RAC Foundation analysis placed the maintenance backlog of nearly 72,000 council-managed road bridges at £6.7 billion, noting that 4.4% of these bridges were substandard and unable to support the heaviest vehicles on UK roads. Earlier data from 2015 and 2021 similarly flagged thousands of substandard bridges requiring significant investment, with repair estimates reaching nearly £1 billion in recent years. Alarmingly, some local authorities have reported that more than half of their bridges are substandard, while ten bridges fully collapsed within a single year, revealing the real and present danger posed by ageing infrastructure.
These challenges are compounded by climate change effects. Recent reports highlight how altered weather patterns—such as heavier rainfall causing flooding and hotter, drier spells leading to ground shrinkage—exacerbate the stress on infrastructure. This puts further strain on the already stretched maintenance regimes and underscores the need for proactive, technologically enhanced management strategies.
Critically, the deployment of AI and digital tools is framed not as a replacement for experienced engineers but as an augmentation. Human expertise remains invaluable for contextualising data and making informed decisions. The role of AI is to enhance the precision, reliability, and efficiency of monitoring and decision-making, transforming inspections from costly, fragmented exercises into predictive, continuous processes that safeguard infrastructure futures.
In sum, the UK faces a pivotal moment in how it manages its ageing infrastructure. Embracing Digital Custodianship—with AI, structured data, sensor technologies, and human insight working in concert—offers a pathway to more resilient, effective asset stewardship. This integrated approach promises not only to arrest the decline of critical structures but also to optimise maintenance investments, improve safety, and harness technological innovation to meet the challenges of the 21st century.
Source: Noah Wire Services
Subscribe to Industry Updates
