Smart Resilient Infrastructure and Urban Systems Lab

Smart Resilient Infrastructure and Urban Systems (SiRIUS) Lab

University of Nebraska-Lincoln

Problems

Modern infrastructure systems face increasing risks from multi-hazard and compounding events, including earthquakes, extreme wind, flooding, and climate-driven disruptions. Existing approaches are often siloed, lack integration across physical and social systems, and fail to provide actionable, real-time decision support. There is a critical need for scalable, data-informed, and system-level frameworks that can quantify resilience, capture interdependencies, and support functional recovery planning at the community scale.

Approach

The SiRIUS Lab develops integrated frameworks that combine physics-based modeling, structural health monitoring, and AI-driven data analytics to quantify and enhance infrastructure resilience. The lab leverages model–data fusion, probabilistic methods, and digital twin technologies to bridge sensing data with computational models. Using platforms such as NIST’s IN-CORE, the research enables multi-scale simulation of infrastructure systems and communities, supporting decision-making under uncertainty.

Findings

Our research demonstrates that integrating sensing data with physics-based models significantly improves the accuracy of structural response estimation and damage assessment. Multi-hazard modeling reveals critical interdependencies that drive system-level failures, while AI-enabled approaches enhance predictive capabilities for recovery and risk mitigation. Applications across seismic, wind, and flood hazards highlight the importance of combining engineering mechanics with data-driven methods for resilience assessment.

Impact

The lab’s work directly informs resilience planning and policy by providing decision-support tools for engineers, planners, and stakeholders. Projects in seismic and wind-prone regions, as well as transportation and community systems, have contributed to improved mitigation strategies and functional recovery planning. The research supports national initiatives (e.g., NSF, NIST) and advances open-source platforms that enable broader adoption of resilience-based design and analysis.

Core Competencies

• Structural health monitoring and digital twins
• Model–data fusion and state estimation
• Multi-hazard risk and resilience modeling
• Probabilistic methods and uncertainty quantification
• AI/ML for infrastructure systems
• Community-scale simulation (IN-CORE)
• Performance-based engineering and functional recovery

Collaborators

Collaborations include NSRI, NIST and NIST’s Center of Excellence for Risk-Based Community Resilience Planning, the IN-CORE platform, and partners across academia and industry including Aon and HDR.

Funding Agencies

National Science Foundation (NSF), National Institute of Standards and Technology (NIST), Nebraska Department of Transportation (NDOT), NASA EPSCoR, Mid-America Transportation Center (MATC)