From predicting potholes to designing more durable concrete, artificial intelligence is paving the way for smarter infrastructure, a University of Mississippi researcher says.
Ali Behnood, assistant professor of civil engineering, has dedicated more than 10 years to this field of study. He has contributed to more than 60 published research articles about the role of artificial intelligence in sustainable infrastructure.
“The goal of our team in the NextGen Infrastructure Lab is to move toward the next generation of sustainable and resilient infrastructure,” he said.
“We’re trying to optimize the use of recycled materials, industrial by-products, renewable resources and alternative sustainable materials in construction while reducing not only physical cost, but labor costs, energy costs, environmental impact costs and lifecycle maintenance expense as well.”
In one of his most recent publications, Behnood and Abolfazl Afshin, an Ole Miss doctoral student in civil engineering from Zahedan, Iran, have tested different artificial intelligence algorithms’ abilities to predict how well asphalt pavements with reclaimed asphalt pavement materials could withstand moisture.
The study is published in the International Journal of Pavement Engineering.
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, Afshin said.
“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,” he said. “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.”
State and local governments spent more than $206 billion on maintaining the nation’s roads in 2021, and the U.S. Department of Transportation in 2023 reported nearly $1 trillion in backlog repairs and maintenance needed for roads and bridges. Optimizing asphalt mixtures could reduce maintenance costs and extend the lifespan of these roads.
Determining the best mixture of reclaimed asphalt pavement and other materials that can withstand wet and cold weather conditions without artificial intelligence would be an incredibly time-consuming and cost-intensive process, Behnood said.
“Artificial intelligence-based algorithms offer a cost-effective and efficient alternative to traditional time-consuming and energy-intensive lab-based approaches,” he explained.
Any entity that wants to develop more sustainable, cost-friendly infrastructure can start using the procedures Behnood’s team developed, he said.
“The results of all these studies can be used by practicing engineers, by the Department of Transportation, federal agencies, private sectors—whoever works in this area—to move toward sustainable, cost-effective approaches in design,” he said. “The tools we develop can be used by any practicing engineer.”
Besides predicting the potential failure of pavements, many other aspects of infrastructure can be streamlined by using artificial intelligence and machine learning, from designing better bridges and roads to waste management and monitoring railroads for faults or breakages, Behnood said.
“AI can also play a crucial role in disaster resilience and risk management,” he said. “In the event of disasters or natural hazards, evacuation becomes critical, and AI can identify optimized routes tailored to various evacuation scenarios, ensuring efficiency and safety.
“There are so many examples of how we can use AI for sustainability in all elements of construction and infrastructure. This is a huge area, and we are doing our little part in this huge area to move toward sustainability and to help society.”
More information:
Abolfazl Afshin et al, Prediction of moisture susceptibility of asphalt mixtures containing RAP materials using machine learning algorithms, International Journal of Pavement Engineering (2024). DOI: 10.1080/10298436.2024.2431610
University of Mississippi
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Research uses AI to make infrastructure more resilient, sustainable (2025, February 12)
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