AI-assisted defect detection for bridge inspection
Digital inspections for urban projects with Haviq Inspect
27. February 2025
In the ever-evolving landscape of infrastructure maintenance, traditional bridge inspections have long been burdened by inefficiencies, high costs, and logistical challenges. Recognizing these issues, the startup hub, DB mindbox, in collaboration with Die Autobahn GmbH, initiated a groundbreaking project to explore the potential of digital twin technology, AI-driven damage analysis, and drone-based inspections. This initiative sought to redefine how bridges are monitored and maintained, paving the way for a more data-driven, precise, and cost-effective approach to structural assessments.
The need for innovation in bridge inspections
Germany’s vast highway network includes approximately 40,000 bridges that require regular inspections. Current methodologies rely heavily on manual inspections, which not only consume considerable time and resources but also necessitate road closures, causing significant disruptions. Additionally, the subjective nature of these assessments often leads to inconsistencies in damage documentation, with no comprehensive historical records available for tracking structural changes over time. These inefficiencies underscore the urgency for a modernized, technology-driven alternative – one that digital twins and AI-powered analytics could potentially provide.
A vision for the future
The project aimed to demonstrate how digital solutions could improve bridge inspections by streamlining data collection, enhancing analysis accuracy, and enabling predictive maintenance. A bridge in Germany, supervised by Die Autobahn, was selected as the test object. The project's primary objective was validating AI-generated damage reports with Twinspect against traditional visual inspections conducted by structural engineers.
Complete digital capture
The first phase of the project involved obtaining regulatory approvals for drone operations. Prior to the official data collection, the drone service provider, Twinisty’s partner, F7 Digital, conducted preliminary site visits to assess the best angles and flight paths. Given the constraints on overflight permissions, images had to be captured at an angle and from a distance, presenting certain modeling challenges.
With approvals in place, drone flights commenced, using advanced imaging technology to capture high-resolution photographs of the bridge’s structure. Despite challenges such as difficult terrain and seasonal instability, F7 Digital successfully captured a seamless 3D model. Over 5,400 images and 34 laser scans were collected, creating a comprehensive dataset for further processing. The collected imagery was then fed into photogrammetry software, RealityCapture, which generated a detailed 3D model within 48 hours. Initially consisting of 500 million vertices, the model was later optimized, reducing processing time while maintaining structural fidelity. This 3D model subsequently uploaded into TWINSPECT consisted of 2.5 million points and 40 8K textures, ensuring a photorealistic and georeferenced representation of the bridge.
AI-powered damage analysis and quality assessment
The AI damage detection process focused on identifying common structural issues such as cracks, corrosion, and graffiti. AI algorithms processed the image data, automatically detecting anomalies and categorizing them based on severity. However, this phase was not without its challenges. The AI model initially struggled with differentiating between noise and actual damage, requiring further refinement.
Twinspect consolidated damages that appear in multiple images, ensuring that the same damage is displayed only once. After the first AI analysis, Twinsity team worked closely with Die Autobahn to review the 600 results in detail and subsequently improved the system through additional training. Finally, the results of AI-driven analysis were then compared with traditional inspections conducted by experienced structural engineers. Out of 176 detected cracks, 156 were identified correctly, resulting in an 88,6% accuracy of the Twinsity AI model.
The future of inspections is undoubtedly data-driven. With Twinspect, we are tackling the challenge of effectively managing the vast amounts of data generated by modern technologies. Our goal is to extract the most relevant insights from this data to provide a solid foundation for decision-making in infrastructure maintenance and upkeep.
This proof of concept has demonstrated the immense potential of AI-powered damage analysis and digital twin technology in transforming bridge inspections. By reducing reliance on manual labor, improving assessment accuracy, and enabling predictive maintenance, Twinspect promises a safer, more cost-effective approach to infrastructure management.
AI-driven damage analysis has proven to be an efficient solution for minimizing workloads, enabling the automated processing of large datasets generated by drone-based inspections. The system consolidates and refines information, ensuring that defects are consistently tracked and compared over time. Unlike the physical object, which is subject to natural deterioration, its digital twin contains historical information. It remains a reliable and consistent reference point. By overlaying past and present data, engineers can gain insights into how objects evolve. This is particularly valuable in infrastructure maintenance and preservation, where tracking minute changes can prevent costly repairs or irreversible damage.
The integration of georeferenced digital twins and AI-driven inspections will likely become the gold standard for structural monitoring. By aligning technology with established standards and regulations, the industry can ensure that inspections are not only more efficient but also more accurate and reliable.
The future of bridge inspections is not only digital – it is data-driven, intelligent, and evolving toward a smarter and more sustainable approach to infrastructure maintenance.
