AI-Powered Tunnel Inspection Introduces a New Era
of Infrastructure Management
AI-Powered Tunnel Inspection Introduces a
New Era of Infrastructure Management
Background
National Highways is responsible for operating, maintaining, and improving England’s
motorways and major roads. As part of this remit, it also manages the Historical Railways Estate
(HRE), a portfolio of more than 3,000 heritage structures including tunnels, bridges, and
viaducts. With many of these assets over a century old, ensuring their safety and reliability
requires both deep engineering expertise and forward-looking innovation.
National Highways is responsible for operating, maintaining, and improving England’s motorways and major roads. As part of this remit, it also manages the Historical Railways Estate (HRE), a portfolio of more than 3,000 heritage structures including tunnels, bridges, and
viaducts. With many of these assets over a century old, ensuring their safety and reliability requires both deep engineering expertise and forward-looking innovation.
National Highways is responsible for operating, maintaining, and improving England’s
motorways and major roads. As part of this remit, it also manages the Historical Railways Estate (HRE), a portfolio of more than 3,000 heritage structures including tunnels, bridges, and viaducts. With many of these assets over a century old, ensuring their safety and reliability
requires both deep engineering expertise and forward-looking innovation.
Challenge
Tunnel inspections are inherently complex. They take place in confined spaces, often with
limited lighting, and require engineers to balance required inspection work with safety
considerations. While traditional methods have long provided valuable insights, National
Highways recognised the opportunity to enhance these practices with advanced digital tools to
support faster, safer, and more detailed inspections, while creating a scalable model for its wider
portfolio.
Tunnel inspections are inherently complex. They take place in confined spaces, often with limited lighting, and require engineers to balance required inspection work with safety considerations. While traditional methods have long provided valuable insights, National Highways recognised the opportunity to enhance these practices with advanced digital tools to support faster, safer, and more detailed inspections, while creating a scalable model for its wider
portfolio.
Tunnel inspections are inherently complex. They take place in confined spaces, often with
limited lighting, and require engineers to balance required inspection work with safety
considerations. While traditional methods have long provided valuable insights, National
Highways recognised the opportunity to enhance these practices with advanced digital tools to support faster, safer, and more detailed inspections, while creating a scalable model for its wider
portfolio.
Solution
InfraMind partnered with National Highways, through HRE, to pilot a digital inspection of an
800m masonry tunnel. The process was straightforward: existing LiDAR survey data was shared
with InfraMind, and our AI platform carried out the full analysis.
The system automatically identified features such as spalling, mortar loss, fractures, and
construction joints with millimetre precision. Crucially, it also measured the size, depth, and
volume of each defect, offering quantitative insights that go beyond traditional visual
assessments. Alongside this, the platform assigned severity ratings, performed deformation
analysis, and integrated historic inspection reports into an interactive 3D digital twin.
Accessible through a browser-based interface, the platform allowed engineers to navigate the
tunnel seamlessly, filter results by defect type and severity, and generate professional inspection
reports instantly. This workflow complemented existing expertise while providing richer, data-
driven visibility
InfraMind partnered with National Highways, through HRE, to pilot a digital inspection of an 800m masonry tunnel. The process was straightforward: existing LiDAR survey data was shared with InfraMind, and our AI platform carried out the full analysis.
The system automatically identified features such as spalling, mortar loss, fractures, and construction joints with millimetre precision. Crucially, it also measured the size, depth, and volume of each defect, offering quantitative insights that go beyond traditional visual assessments. Alongside this, the platform assigned severity ratings, performed deformation analysis, and integrated historic inspection reports into an interactive 3D digital twin.
Accessible through a browser-based interface, the platform allowed engineers to navigate the tunnel seamlessly, filter results by defect type and severity, and generate professional inspection reports instantly. This workflow complemented existing expertise while providing richer, data-
driven visibility
InfraMind partnered with National Highways, through HRE, to pilot a digital inspection of an
800m masonry tunnel. The process was straightforward: existing LiDAR survey data was shared with InfraMind, and our AI platform carried out the full analysis.
The system automatically identified features such as spalling, mortar loss, fractures, and
construction joints with millimetre precision. Crucially, it also measured the size, depth, and
volume of each defect, offering quantitative insights that go beyond traditional visual
assessments. Alongside this, the platform assigned severity ratings, performed deformation analysis, and integrated historic inspection reports into an interactive 3D digital twin.
Accessible through a browser-based interface, the platform allowed engineers to navigate the
tunnel seamlessly, filter results by defect type and severity, and generate professional inspection reports instantly. This workflow complemented existing expertise while providing richer, data-driven visibility
Results
The pilot showed how AI can add value at every level of asset management. Inspections that
once took weeks were processed in hours, reducing site exposure and freeing engineers from
manual reporting. Precise and objective defect measurements allows asset managers to prioritise
repairs objectively, optimise maintenance schedules, and allocate budgets more effectively. At a
strategic level, the platform provides early visibility of deterioration, reducing the risk of
unexpected failures and supporting longer asset lifespans.
Together, these outcomes demonstrate that AI-powered inspection is not just a faster way to
survey tunnels, it is a smarter, data-driven approach to managing and protecting infrastructure at
scale.
The pilot showed how AI can add value at every level of asset management. Inspections that once took weeks were processed in hours, reducing site exposure and freeing engineers from manual reporting. Precise and objective defect measurements allows asset managers to prioritise repairs objectively, optimise maintenance schedules, and allocate budgets more effectively. At a strategic level, the platform provides early visibility of deterioration, reducing the risk of unexpected failures and supporting longer asset lifespans.
Together, these outcomes demonstrate that AI-powered inspection is not just a faster way to survey tunnels, it is a smarter, data-driven approach to managing and protecting infrastructure at scale.
The pilot showed how AI can add value at every level of asset management. Inspections that
once took weeks were processed in hours, reducing site exposure and freeing engineers from manual reporting. Precise and objective defect measurements allows asset managers to prioritise repairs objectively, optimise maintenance schedules, and allocate budgets more effectively. At a strategic level, the platform provides early visibility of deterioration, reducing the risk of unexpected failures and supporting longer asset lifespans.
Together, these outcomes demonstrate that AI-powered inspection is not just a faster way to
survey tunnels, it is a smarter, data-driven approach to managing and protecting infrastructure at scale.
Client Validation
National Highways validated InfraMind’s outputs against prior inspection records and confirmed
their accuracy and added value. The AI analysis closely aligned with previous findings while
providing more consistency and depth. On the strength of this pilot, National Highways directed
InfraMind to work with Jacobs on a Business-as-Usual solution and began preparing for
expansion across additional tunnel structures.
“Partnering with InfraMind has been a true collaborative effort. Their platform is the first
solution we’ve seen that can analyse tunnel data in this way. Through this project, we’ve
experienced first-hand that AI is no longer just a concept - it’s delivering real, measurable
value for safety, planning, and maintenance.”
-Colin Mcnicol, National Highways
National Highways validated InfraMind’s outputs against prior inspection records and confirmed their accuracy and added value. The AI analysis closely aligned with previous findings while providing more consistency and depth. On the strength of this pilot, National Highways directed InfraMind to work with Jacobs on a Business-as-Usual solution and began preparing for expansion across additional tunnel structures.
“Partnering with InfraMind has been a true collaborative effort. Their platform is the first solution we’ve seen that can analyse tunnel data in this way. Through this project, we’ve experienced first-hand that AI is no longer just a concept - it’s delivering real, measurable value for safety, planning, and maintenance.”
-Colin Mcnicol, National Highways
National Highways validated InfraMind’s outputs against prior inspection records and confirmed their accuracy and added value. The AI analysis closely aligned with previous findings while providing more consistency and depth. On the strength of this pilot, National Highways directed InfraMind to work with Jacobs on a Business-as-Usual solution and began preparing for expansion across additional tunnel structures.
“Partnering with InfraMind has been a true collaborative effort. Their platform is the first
solution we’ve seen that can analyse tunnel data in this way. Through this project, we’ve
experienced first-hand that AI is no longer just a concept - it’s delivering real, measurable
value for safety, planning, and maintenance.”
-Colin Mcnicol, National Highways
What’s Next
Following the pilot, InfraMind and National Highways are preparing for further trials, including
an upcoming project on a major bridge. These trials will explore how the platform can be applied
to a wider range of assets beyond tunnels and demonstrate its potential to support broader digital
approaches to infrastructure management in the future.
Following the pilot, InfraMind and National Highways are preparing for further trials, including an upcoming project on a major bridge. These trials will explore how the platform can be applied to a wider range of assets beyond tunnels and demonstrate its potential to support broader digital approaches to infrastructure management in the future.
Following the pilot, InfraMind and National Highways are preparing for further trials, including an upcoming project on a major bridge. These trials will explore how the platform can be applied to a wider range of assets beyond tunnels and demonstrate its potential to support broader digital approaches to infrastructure management in the future.
About InfraMind
InfraMind is a Cambridge spin-out transforming the way critical infrastructure is inspected and
maintained. By applying our proprietary pre-trained models to multi-modal remote sensing data,
digitising survey data, integrating historical records, and applying advanced AI, InfraMind
provides asset owners with faster, safer, and more cost-effective ways to understand and protect
their networks.
InfraMind is a Cambridge spin-out transforming the way critical infrastructure is inspected and maintained. By applying our proprietary pre-trained models to multi-modal remote sensing data, digitising survey data, integrating historical records, and applying advanced AI, InfraMind provides asset owners with faster, safer, and more cost-effective ways to understand and protect their networks.
InfraMind is a Cambridge spin-out transforming the way critical infrastructure is inspected and
maintained. By applying our proprietary pre-trained models to multi-modal remote sensing data, digitising survey data, integrating historical records, and applying advanced AI, InfraMind
provides asset owners with faster, safer, and more cost-effective ways to understand and protect their networks.
Discover how InfraMind can transform your inspections.
Book a demo to see how our AI technology detects structural issues and prevents costly failures
Discover how InfraMind can transform your inspections.
Book a demo to see how our AI technology detects structural issues and prevents costly failures
Discover how InfraMind can transform your inspections.
Book a demo to see how our AI technology detects structural issues and prevents costly failures