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AI USE IN INSPECTION
AI Used in Transportation Inspection
Kyle Dickinson
Civil, Architectural and Environmental Engineering Department,
Missouri University of Science and Technology
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AI USE IN INSPECTION
AI in Transportation Inspection
Artificial Intelligence (AI) is becoming a bigger ang bigger part of the lives of people all around
the world. One way that Artificial Intelligence is becoming bigger is in the inspection of
transportation infrastructure. This includes but is not limited to roadways, bridges, and tunnels.
AI is a tool that inspectors can use to inspect this infrastructure faster, safer and more accurately.
AI is changing the way inspections can be done by being faster, safer, less expensive, and
lowering the likely hood of human error.
The first way that AI helps is by being able to analyze images. An example of this, written about
by AL Shaffian in 2024, is a program that can detect cracks in concrete. It is even able to do this
in non-ideal situations such as low lighting or distractions in the background. Another AI tool,
written about by Abdel-Qader in 2005, is called Principal Component Analysis (PCA). This
program breaks the imagines down into smaller parts, so it is easier for the AI to detect damage
within the structures of the bridges. Another inspection method is using drones to fly over
runways and take images. These images along with control points are used to create digital
elevation models (DEMs). Essentially making a topographic map from images taken with a
drone (McNerney, 2024). This can then be used to look for cracks or tips in runways at airports.
These are methods that use AI to inspect bridges without harming them.
The second benefit of the methods used are they don’t have to harm the structure or slow down
travel. This method is referred to as non-destructive testing (NDT). An example of this is AI
being able to analyze images instead of someone having to go and possibly cut into the material
to inspect it (Abdel-Qader, 2005). Another great example of this is drones flying over runways to
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AI USE IN INSPECTION
take pictures that can later be analyzed. This can be done without shutting down the Airport
(McNerney, 2024). There are a lot of rules and regulations to follow the FAA rules, but it beats
having to shut down the entire airport to have an inspector go out and physically inspect the
runway. Another nondestructive method is using a mixed reality headset. This shows real time
feed of the site, and AI is constantly analyzing the images and highlighting any possible
problems. (Zakaria, 2023). These new AI non-destructive testing methods are less invasive,
faster, and can be more through than older methods.
Using AI to do inspections is also more efficient than older methods. Using AI no longer dose a
person have to go out and physically inspect infrastructure. Now they can simply go out take
pictures of the road, bridge, or whatever they are inspecting, upload the images and have them
combed over by AI looking for problems (Al Shafian, 2024). Airports no longer have to be shut
down so an inspector can go walk the runway looking for any problem areas. Now a drone,
following FAA regulations, can simply fly over while the airport is still operational. AI can than
instantly analyze these images looking for any sort of problems areas on the runways or taxi
ways (McNerney, 2024). Along with mixed-reality saving time by highlighting problem areas
that can be inspected further. (Zakaria, 2023). This allows for inspections to be done faster,
allowing for them to be done more often. This allows for problems to be found sooner and fixed
before they worsen.
Conclusion
These new methods, powered by AI, of inspection are the future of transportation inspection.
They are more efficient, don’t damage the piece of infrastructure, and allow for another degree of
redundancy when doing inspections. All these benefits allow for transportation infrastructure to
be better benefiting everyone.
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AI USE IN INSPECTION
References
Abdel-Qader, I., Pashaie-Rad, S., Ahmed, K., & Abudayyeh, O. (2005). A local PCA algorithm
for inspection of Concrete Bridges. Computing in Civil Engineering (2005), 1–7.
https://doi.org/10.1061/40794(179)37
Al Shafian, S., Hu, D., & Yu, W. (2024). Deep Learning Enhanced Crack Detection for tunnel
inspection. International Conference on Transportation and Development 2024, 732–741.
https://doi.org/10.1061/9780784485514.064
McNerney, M. T., Bishop, G., & Saur, V. (2024). Using AI and changing detection in geospatial
UAS Airfield Pavement Inspection for Pavement Management. International Conference on
Transportation and Development 2024, 876–883. https://doi.org/10.1061/9780784485514.077
Zakaria, M., Karaaslan, E., & Necati Catbas, F. (2023). Real-time AI-based bridge inspection
using mixed reality platform. Structures Congress 2023, 120–131.
https://doi.org/10.1061/9780784484777.012