Asbestos may feel like a problem of the past, but its legacy still lingers across the UK’s road and transport infrastructure. Many structures built prior to the 2000 asbestos ban—bridges, tunnels, lighting systems, drainage ducts, and roadside cabinets—still contain asbestos-containing materials (ACMs). These hidden hazards pose significant risks to highway workers, contractors, and in some cases, the travelling public.
In response, the UK Highways Agency (now National Highways) established the General Asbestos Management Plan (GAMP) to comply with Regulation 4 of the Control of Asbestos Regulations (CAR) 2006. Yet despite these strong regulatory measures, traditional asset inspections depend heavily on manual surveys—slow, expensive, and highly variable. Today, technology platforms such as RoadVision AI are helping upgrade inspections through AI-based road network monitoring systems. As the saying goes, “Work smarter, not harder.”
Even more than 20 years after the ban, asbestos remains embedded within older highway assets. Left unmanaged, ACMs can degrade due to weathering, vibration, or structural fatigue—creating serious health risks for maintenance staff.
Key reasons why asbestos detection must remain a national priority
Worker Safety
Prevents accidental disturbance of ACMs during routine maintenance through better infrastructure condition monitoring.
Regulatory Compliance
CAR 2006 requires asset owners to identify, record, and actively manage asbestos risks.
Long-Term Cost Savings
Early detection prevents emergency closures, remediation surcharges, and litigation costs.
Asset Life Extension
Ensures bridges, tunnels, and transport structures remain safe and serviceable.
With the Strategic Road Network continuing to modernise, asbestos detection is no longer optional—it is a critical public safety function.
The General Asbestos Management Plan (GAMP) provides a structured national framework for asbestos management across the UK road network. Its principles align closely with the Control of Asbestos Regulations (CAR) 2006.
Core GAMP Principles
2.1 Surveying and Identification
Systematic inspection of all highway assets built before 2000.
2.2 Recording and Reporting
Uploading survey findings into digital systems such as SMIS, HAPMS, or TPMS.
2.3 Risk Assessment
Classifying ACMs based on condition, location, disturbance probability, and exposure risk.
2.4 Asbestos Action Plans (AAPs)
Each asset class—bridges, tunnels, gantries, depots—requires a structured mitigation plan.
2.5 Annual Coverage Targets
At least 5% of assets must be inspected annually, reaching full coverage by 2025.
2.6 Monitoring and Review
Continuous condition tracking supported by modern digital road inspection tools.
These principles form the UK’s gold-standard compliance framework, but relying solely on manual implementation slows progress.
Digital and AI-based inspection tools such as RoadVision AI enhance asbestos detection by making surveys faster, safer, and more consistent.
3.1 High-Resolution Imaging & AI-Driven Material Recognition
Survey vehicles, drones, or dashcams capture high-resolution images of infrastructure assets.
AI models can identify potential ACM indicators including:
These insights help prioritise assets for specialist asbestos inspections.
3.2 Seamless Data Integration with National Highways Systems
AI inspection platforms can:
This ensures rapid visibility across compliance and asset management teams.
3.3 Geo-Tagging & Predictive Risk Mapping
Using asset age, construction records, and previous inspections, AI systems generate:
This improves planning through AI-based infrastructure analytics.
3.4 Digital Twins for Remote Condition Monitoring
Digital twins of bridges, tunnels, and substations now include:
Teams can simulate risks such as water ingress or structural movement while supporting long-term transport infrastructure monitoring.
In short, technology converts static asbestos records into dynamic, real-time intelligence.
Despite their advantages, digital asbestos monitoring systems face several practical challenges.
4.1 Legacy Infrastructure
Many older highway assets lack complete construction documentation.
4.2 Model Training Requirements
AI systems must be trained using UK-specific infrastructure material datasets.
4.3 Data Security & Compliance
Sensitive infrastructure data requires strict cybersecurity and regulatory safeguards.
4.4 Workforce Familiarity
Survey teams must transition from manual inspections to digital workflows.
4.5 Procurement Cycles
Adoption of AI platforms, drones, and imaging technologies may require long approval processes.
However, modern AI-enabled inspection platforms help agencies overcome these barriers through scalable automation.
As the UK continues modernising its transport infrastructure, asbestos management must evolve beyond traditional survey methods. Tech-enabled road inspections provide a faster, more scalable, and far more reliable solution for identifying and managing asbestos risks across the Strategic Road Network.
By combining artificial intelligence, geospatial mapping, and digital asset databases, the UK can shift from reactive asbestos discovery to proactive risk prevention.
As the proverb says, “Forewarned is forearmed.”
Digital platforms such as RoadVision AI support this transition by delivering:
Through advanced AI infrastructure monitoring systems, agencies can protect workers, reduce long-term remediation costs, and maintain safer roads for the future.
For councils, consultants, and National Highways contractors, the future of asbestos detection is no longer manual—it is intelligent.
An AAP outlines identified or potential asbestos risks in highway assets and includes steps for safe handling, remediation, and compliance with CAR 2006.
AI analyzes visual and geospatial data to flag potential ACMs in roads, bridges, and tunnels—especially in older assets built before the asbestos ban.
Common assets include tunnel cladding, drainage pipes, lighting columns, bridge deck materials, and control cabinets installed before 2000.
The Autonomous Road Engineer
AI that is transforming road infrastructure, from reactive to predictive.
