Dr. Arghavan Louhghalam receives NSF CAREER award

The National Science Foundation has awarded Dr. Louhghalam $524,940 to support research that will lead to significant reductions in economic loss and negative social impacts of natural hazards.

Assistant Professor of Civil & Environmental Engineering
Dr. Arghavan Louhghalam, Assistant Professor of Civil & Environmental Engineering at UMass Dartmouth

Resilience is the capacity of a system to absorb a shock and recover from it without a significant loss of performance. For civil infrastructure systems subject to extreme events, this performance is the functional integrity of lifeline infrastructures (power, transportation, water, etc.) and other buildings and structures comprising the built environment. 

The National Science Foundation has awarded Dr. Arghavan Louhghalam, Assistant Professor of Civil and Environmental Engineering, $524,940 over a five-year period, for her research titled “CAREER: An Integrated Framework for Resilience Analytics: From Physics-based Modeling of Building Components to Dynamics of Community-Level Recovery.” The project will address a significant knowledge gap, on how to rigorously define and quantitatively but efficiently evaluate damage and performance at different scales to systematically assess resilience.

The frequency of natural hazards with great economic and social impact—such as tropical cyclones, severe storms, and earthquakes—has increased significantly in recent years. “Strategies that aim at evaluating the ability of a particular built environment to cope with these abnormal events are sought by policymakers and governmental organizations such as DHS, FEMA, and DOD,” Louhghalam says.

“The goal is to develop a predictive framework that enables the next generation of infrastructure resilience analytics and addresses the complexities associated with multiscale, multifaceted, and probabilistic nature of resilience,” says Louhghalam. Louhghalam will achieve this through integrating statistical physics-based models of failure and damage, smart and optimal Bayesian learning in stochastic space, and system dynamics to address the kinetics of recovery.

“My plan is to push the boundaries of infrastructure resilience modeling beyond the current conceptual approaches and ad hoc solutions by providing a systematic approach to defining and evaluating the “performance functions" while accounting for the underlying uncertainties in a multi-scale (from component to the community) setting,” notes Louhghalam. “The outcome of this research would provide a means for policymakers for risk-informed decision-making and facilitate devising strategies that enhance the ability of civil infrastructures to cope with the extreme events and as such will lead to significant reductions in economic loss and negative social impacts of natural hazards.”



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