Fairfield University’s School of Engineering and Computing, along with a collaborator from The University of Mississippi, has been awarded a grant of nearly $400,000 from the National Science Foundation.

Each year, communities are experiencing an increase of extreme weather events such as droughts, earthquakes, floods, hurricanes, tornadoes, and fires. Given the projections of continued climate change, there is an urgency for disaster resilience strategies to address these environmental hazards. The grant, entitled “Disaster Resilience Research,” in partnership with the University of Mississippi, will develop an artificial intelligence model to analyze remote sensing data for monitoring the failure risk profile of mine tailings impoundments following exposure to extreme weather events.  

Mine tailings are the waste product of mining — typically composed of sand, silt, and clay particles, suspended in a water-based slurry. They are stored in man-made or natural impoundments, with high walls and seepage control to prevent the tailings from escaping. Extreme weather conditions can stress these structures and put them at risk of failure.

"The successful implementation of our research will equip at-risk communities with crucial tools for managing failures and enhance the knowledge base for impoundment monitoring, guiding future policies to improve monitoring standards," explained assistant professor of computer science Sidike Paheding, PhD.

The project's objectives include: 

• Studying how satellite radar and moisture measurements can help us understand how mine tailings impoundments perform over time.
• Using engineering principles and satellite data to study the lifespan of tailings impoundments.
• Creating guidelines to assess the risk of failure in mine tailings impoundments using advanced AI models and satellite data, along with environmental and extreme weather information.

Disaster resilience research has interdisciplinary implications for remote sensing, computer science, and geoengineering. The development of an AI model will be cost-effective and will allow for non-intrusive monitoring methodology of to predict extreme weather and mitigate infrastructure failures. 

Learn more about the School of Engineering and Computing at fairfield.edu/engineering.