AI helps size up potholes and road durability
Researchers at the University of Mississippi have developed AI algorithms to predict the durability of road surfaces and likelihood of potholes developing.
The study, published in the International Journal of Pavement Engineering, tested different algorithms’ abilities to predict how well asphalt pavements with reclaimed materials could withstand moisture. When water seeps into asphalt, it can break the bonds that hold the materials together. In its weakened state, the asphalt is more likely to crack or otherwise fail, according to the researchers.
“We focused on moisture damage, which is one of the most critical issues in asphalt pavements, particularly for wet and cold regions, because it results in a variety of distresses like stripping, potholes and cracking,” said Afshin Abolfazl Afshin, a Mississippi doctoral student in civil engineering. “We evaluated the effectiveness of four different artificial intelligence algorithms in predicting moisture damage in asphalt mixtures containing (reclaimed asphalt pavement) materials.
“What we found was that these algorithms are able to effectively predict moisture damage in asphalt mixtures with high accuracy. Based on these results, we can optimize material selection and predict failure probability in the pavement’s life cycle.”
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