UCF’s ‘bridge doctor’ combines imaging, neural network to efficiently evaluate concrete bridges’ safety

(Press-News.org) Necati Catbas doesn’t hold a medical degree, but the UCF engineering professor is more than qualified to diagnose the health of bridges using a combination of emerging technologies.

Catbas collaborated with his former civil engineering student Marwan Debees ’23PhD, who now works as a NASA Bridge Program manager, on newly published research that details how infrared thermography, high-definition imaging and neural network analysis can combine to make concrete bridge inspections more efficient.

Catbas and Debees are hopeful that their findings, recently published in the Transportation Research Record, can be leveraged by engineers through a combination of these methods to strategically pinpoint bridge conditions and better allocate repair costs.

“If we better understand which bridges need more repairs and which bridges may be postponed, then [funding agencies] can use limited funds more wisely, and then we can direct our efforts to the really critical bridges,” Catbas says. “We have about 650,000 bridges in the U.S. and we have been working to examine how we can use novel technologies to understand the existing condition of structures.”

Debees noted an instance during a NASA bridge load test where Catbas and his team assisted in evaluating the repairs. They determined that the repairs made were sufficient, ultimately, eliminating the next phrase of planned work.

“We’re only spending the money where we need to instead of doing it without a comprehensive understanding of the actual conditions of the bridge in the field,” Debees says. “The goal is to better understand the conditions of the bridge and have a better priority list of what bridges are really in need.”

Diagnosing Concrete Bridges

Catbas says what he and other civil engineers do to assess a structure’s overall integrity may be likened to a doctor’s diagnostics for a person’s wellbeing.

“Structural health monitoring, which is almost like human health monitoring, is where we use different types of equipment to better understand the safety and serviceability of structures,” he says.

To help take high-definition images to compare to infrared data, the researchers closely collaborated with NEXCO-West USA. Inc, an imaging and non-destructive evaluation company in Tysons, Virginia, that have specialized vehicles equipped with imaging tools. With the company’s support, the research team utilized the infrared data to assess the conditions of bridge components, including the deck, superstructure and substructure.

“As far as the infrared itself, there are some limitations,” Debees says. “One of the things in this paper that helped overcome some of these limitations is high-definition images to complement the infrared images.”

These technologies that were used in the study by Catbas and Debees provided a more comprehensive record of concrete bridge health.

“Human visualization has limitations,” Catbas says. “It’s almost like a doctor just looking at you and saying that you look fine when you might really be fine, or you might not be. There may be other problems that the sensors and other technologies can tell you, kind of like when a doctor says he wants more testing, so he sends you to get an X-ray or an MRI. We are taking a similar approach to our bridges.”

Bridging the Gap Between Technology and Interpretation

Infrared thermography works by collecting a structure’s thermal responses, which can indicate defects within it such as heat loss, moisture intrusion or other structural problems.

To analyze the different parts of the bridge such as the deck, superstructure and substructure, the research team used thermography and image capturing technologies deployed on boats under the bridge and on vehicles traveling across it so that traffic wouldn’t be impeded and motorists may continue using the roads.

The combination of visual inspection and imaging is common practice, but Debees says the element of utilizing a neural network and machine learning to decipher the data is something that is an emerging component of inspections. The collective knowledge from experienced engineers doing similar inspections was used to compare the results in the study.

“The way it differs from other utilization is that we are not using just infrared cameras and collecting raw data, but then we have a level of post-processing, and we are eliminating the noise or unnecessary information within the infrared image,” Debees says. “Then we use this data to understand where these defects are and then we integrate them within the current required bridge inspection processes. We close the loop by using some decision-making and algorithms with an easy-to-use perceptron neural network to guide the inspector or engineer without spending too much time or data analysis.”

The two parts of the paper are how to implement this new technology and how it can be used to accelerate decision making while keeping it accurate and safe, he says.

“When we do bridge inspections, we aim to find ways to accelerate or make it more efficient while also having more data to rely on in the future or in the immediate decision making,” Debees says. “We can determine which bridge needs to be evaluated right away, which needs more testing and we can see the significance of the finding quicker.”

Crossing Into the Future

Debees says one of the most exciting parts of the research findings is the realization that the framework of multiple inspection techniques can be integrated with collective knowledge and applied to monitor a wide variety of structures.

“We’re not limited to concrete bridges,” he says. “We can build on this research and applying it with different inspection methods and use it for different infrastructure types. We can try this on concrete buildings, or steel bridges, buildings or other structures.”

Using machine learning and collective knowledge to interpret data is something that Debees believes will continue to have a role in inspections even beyond the purview of their study.

“I think what was eye-opening to me is there is room, even outside of conventional inspections, to utilize more decision-making neural networks to standardize the decision-making [process],” he says. “You can make it easier on the people in the field to know where to make decisions on the spot or where to seek more experienced help.”

There are ample opportunities to discover even more innovative ways to assess structural health, and Catbas says he gladly looks forward to meeting the next challenge with former students and collaborators like Debees.

“Like other Ph.D. students of mine, we still keep in touch once they graduate and then become my colleague,” Catbas says as he turns to Debees. “So, my question is this: ‘What are we going to work on next?’”

Researchers’ Credentials:

Catbas holds a doctorate in structural engineering from the University of Cincinnati. After postdoctoral studies at Drexel University in Philadelphia, he joined UCF’s College of Engineering and Computer Science in 2003 and is the founding director of the Civil Infrastructure Technologies for Resilience and Safety (CITRS) Initiative. His research covers various aspects of civil engineering, including analysis, design, and assessment of civil infrastructure systems, structural health monitoring, structural identification, structural dynamics, and earthquake engineering.

Debees graduated in 2023 from UCF with a doctoral degree in civil engineering. His research focuses on structural engineering, particularly on bridge systems. His work emphasizes the application of technology in bridge assessment and the efficacy of structural repairs. Debees currently serves as the Bridge Program Manager at NASA, where he has worked since 2013. Prior to joining NASA, he spent three years with Manson Construction.

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