Between June 2023 and February 2025, HumanTech partners from different organisations gathered at a highway bridge in Kleinandelfingen, Switzerland, to test our semi-autonomous system for bridge inspection and monitoring. This pilot is part of our broader ambition to digitalise the construction sector — enhancing safety, efficiency, and sustainability through innovative technologies.
Our goal was to identify an affordable drone system with maximum technological support to simplify bridge inspections — crucial for maintaining ageing infrastructure and addressing the growing shortage of skilled inspectors and experienced drone pilots. With this in mind, we aimed to create a best practice demonstrating how future inspections and analyses could be conducted.
This pilot — one of our five HumanTech Pilots — represents an important step toward digitalising inspection processes through robotics, paving the way for more efficient and AI-supported methods in construction monitoring.
Read the insights from the team behind its development:
How HumanTech is digitalising bridge inspections
Sebastian Mattes, Reality Capturing Specialist at Implenia
As part of the HumanTech project at Implenia, we are focusing on bridge inspections. Our ageing infrastructure — particularly bridges — needs more and more attention. At the same time, we face a shortage of skilled labour, as many experienced inspectors are reaching retirement age.
To reduce the workload and improve efficiency, we have developed a best practice that combines drones and AI in a digital system, enabling semi-autonomous inspection and evaluation.
The system allows for the overlay of previous inspection data, making it possible to accurately track changes in cracks over time. This process is designed to support and enhance the work of human inspectors.
“To reduce the workload and improve efficiency, we have developed a best practice that combines drones and AI in a digital system, enabling semi-autonomous inspection and evaluation.”
Custom drone technology for precision infrastructure monitoring
Michael Peter, Patipol Thanuphol, Research Associate at ZHAW
The Control Systems Group at the Institute of Mechatronic Systems (ZHAW) has developed a companion system for the DJI Mavic 3E drone, designed to overcome the challenge of capturing images underneath the bridges where GNSS signals are unavailable.
Traditionally, photogrammetry in these environments has required manual piloting, but ZHAW’s system leverages drone-based visual SLAM to enable stable positioning, making operations more reliable and significantly reducing the burden on the pilot.
The custom hardware setup includes a high-resolution camera with flash, a microcontroller with Bluetooth and Wi-Fi, laser pointers for field-of-view indication, and a receiver for remote triggering. This solution extends the capabilities of state-of-the-art commercial drone technology, allowing for capturing perpendicular images, regardless of camera orientation, even under challenging conditions.
“This solution extends state-of-the-art commercial drone technology to capture perpendicular images, regardless of camera orientation, under difficult conditions.”
As a proof of concept, the system demonstrates that high-quality results can be achieved without advanced piloting skills, highlighting ZHAW’s expertise in merging advanced control systems with accessible commercial platforms. Beyond solving current challenges, this development also opens the door to future autonomous solutions for infrastructure inspection.
AI-powered 3D modelling and defect detection for structural inspections
Jo Luick, Project Lead at STRUCINSPECT
STRUCINSPECT specialises in advanced structural inspection solutions, leveraging cutting-edge technology to help ensure the safety and longevity of critical infrastructure.
In HumanTech, we used photogrammetric processes to create a precise 3D replica of a bridge by analysing images captured by two different data acquisition systems. The process included georeferencing and scaling to ensure accuracy, resulting in two 3D models that allowed for a detailed comparison.
We then applied automated AI-driven defect detection to identify structural issues such as cracks, spallings, exposed reinforcements, corrosion, and efflorescence. These detected defects were mapped onto the 3D model and exported in the Industry Foundation Classes (IFC) format, creating a comprehensive digital representation of the bridge’s condition.
This method not only improves the accuracy of structural assessments but also streamlines the inspection workflow, making defect documentation more efficient. Integrating AI and photogrammetry marks a significant advancement in structural inspection practices.
“This method not only improves the accuracy of structural assessments but also streamlines the inspection workflow, making defect documentation more efficient.”
Turning bridge data into digital models
Marius Schellen, Researcher at RPTU
In addition to overseeing the progress of this pilot, RPTU was responsible for coordinating its development and implementation. On the technical side, RPTU provided an approach for BIM reconstruction based on the scanned data. To support this, results from previous research were adapted and applied to explore methods for semantic segmentation and scan-to-BIM reconstruction. A demonstration of the BIM reconstruction will be included as part of the pilot. However, further development is needed to fully automate the process.
This pilot demonstrates how innovation across disciplines can transform infrastructure inspection. By addressing real-world challenges, HumanTech is paving the way for smarter, safer and more sustainable construction practices. The results from this pilot not only showcase the potential of semi-autonomous systems today, but also lay the groundwork for future fully automated solutions.
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