BOZEMAN — Cracks in the roads are common to see when driving.
However, a team at MSU is pioneering a more efficient method for assessing the severity of them.
Kelvin Wang, who has been conducting pavement data research for nearly 30 years, emphasizes the significance of this development.
“Fifty, sixty years ago, we sent people to the moon. And today we couldn’t even figure out where the cracks are, how bad they are, so I said, ‘we need to do some research in this,’” said Wang.
WATCH: Driving Innovation: MSU's 3D truck uses lasers and AI for road assessment
Wang's latest project features a state-of-the-art 3D truck equipped with lasers and high-resolution cameras.
The vehicle can capture detailed images of pavement while traveling at highway speeds. Artificial intelligence processes these images, allowing researchers to more efficiently and effectively identify issues such as cracking.
"The condition of pavement has been a critical problem for decades," Wang explained. "Since World War II, people have dedicated their careers to pavement evaluation."
Trillions of dollars have been invested in paving roads, airfields, and other infrastructures, according to Wang.
The MSU team believes their innovative approach is vital for transportation agencies and the military, particularly for assessing runway conditions during battles.
Neil Hetherington, a member of Wang's research team, provided insights into the technology. While driving around the parking lot, he pointed out on the screen a long crack we had passed.
He elaborated on the function of the truck's sensors, stating that the lasers and cameras at the front act as safety sensors to measure road friction, while those at the back are specifically designed to detect cracks.
Hetherington noted that current evaluation methods for pavement are often slow and labor-intensive.
The goal of their project is to streamline this process, enabling assessments to be conducted at highway speeds over considerable distances.
For Clayton Donally, an MSU engineering graduate student working with Wang, this project has been especially rewarding.
"Getting to be hands-on and understand the actual process of the data collection and how it works is pretty neat," he said.
Looking ahead, Wang said his team hopes to secure additional funding to further develop and implement their groundbreaking technology.
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