In the face of mounting global infrastructure repair costs and a pressing need for sustainability, artificial intelligence (AI) is emerging as a game-changer. A study by Ali Behnood, Assistant Professor of Civil Engineering at the University of Mississippi, demonstrates how AI can optimize the use of recycled materials in construction, reducing costs and environmental impact while enhancing durability.
Behnood’s research, which has contributed to over 60 published articles, focuses on the development of AI algorithms to predict moisture damage in asphalt pavements containing reclaimed asphalt pavement (RAP) materials. This is particularly crucial for wet and cold regions where moisture-related distresses such as stripping, potholes, and cracking are prevalent.
The findings suggest that these AI algorithms can accurately predict moisture damage in asphalt mixtures containing RAP materials, thereby optimizing material selection and extending the lifespan of roads. With state and local governments spending over $206 billion on road maintenance in 2021 and a reported $1 trillion backlog in repairs and maintenance needed for U.S. roads and bridges, these cost-effective predictions could significantly reduce maintenance costs.
Beyond asphalt pavements, AI’s applications in infrastructure span various sectors, from designing more durable concrete to monitoring railroads for faults or breakages. Moreover, AI can play a crucial role in disaster resilience and risk management by identifying optimized evacuation routes during emergencies.
As Behnood emphasizes, the potential for AI in promoting sustainability across all construction and infrastructure elements is vast, with his team’s tools being accessible to practicing engineers, government agencies, and private sectors alike. The future of infrastructure lies in harnessing the power of AI for a more sustainable, cost-effective, and resilient world.
The team’s research focuses on optimizing the use of recycled materials, industrial by-products, renewable resources, and alternative sustainable materials in construction. In a recent study, Behnood and his colleague, Ole Miss doctoral student Abolfazl Afshin, tested AI algorithms’ ability to predict the durability of asphalt pavements containing reclaimed asphalt pavement (RAP) materials.
Water seepage can weaken asphalt, making it more susceptible to cracking and failure. By using AI to predict moisture damage in asphalt mixtures with RAP materials, the team found that these algorithms could effectively predict such damage with high accuracy. This insight can help optimize material selection and extend the lifespan of roads, potentially reducing maintenance costs.
Determining the optimal mixture of RAP and other materials without AI would be a time-consuming and costly process. However, AI-based algorithms offer a more efficient and cost-effective alternative, Behnood explained. The results of these studies can be applied by engineers, government agencies, and private sectors to develop sustainable and cost-friendly infrastructure solutions.
Beyond asphalt pavements, AI can streamline various aspects of infrastructure development, from designing more durable bridges and roads to waste management and monitoring railroads for faults or breakages. Moreover, AI can play a crucial role in disaster resilience and risk management by identifying optimized evacuation routes during emergencies.
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