How users, physical networks, and environmental stressors interact and evolve. These interactions and their hidden relationships shape long-term resilience and sustainability.
The question
Infrastructure is not only a physical network; it is a socio-infrastructure system whose behavior emerges from the interplay of users, infrastructure owners, components, and environmental stressors over time. Static, purely physical models miss the hidden, dynamic relationships. We want to understand how user behavior, network topology variations, hazard temporality, and human adaptability drive long-term resilience and sustainability.
What we do
We represent infrastructure as dynamic attributed systems that encode how user behavior and social context influence network performance, showing that ignoring these effects biases resilience estimates. We draw on network science, decision-making under deep uncertainty methods, and machine-learning algorithms to expose non-trivial interactions across a system’s lifecycle. Our goal is to promote dynamic infrastructure systems modeling that helps plan sustainable, resilient, and equitable futures.
Publications
2025
ICOSSAR’25
Attributed Graphs Preserve User Impacts on Network Performance Computation
Raul Rincon, Jamie Ellen Padgett, and Leonardo Duenas-Osorio
In 14th International Conference on Structural Safety and Reliability (ICOSSAR’25), 2025
The functionality of interdependent infrastructure and resilience to seismic hazards has become a topic of importance across the world. The ability to optimize an engineered solution and support informed decision-making is highly dependent on the availability of comprehensive datasets and requires substantial effort to ingest into community-scale models. In this article, a comprehensive seismic resilience modeling methodology is developed, with detailed multi-disciplinary datasets, and is explored using the state-of-thescience algorithms within the interdependent networked community resilience modeling environment (IN-CORE). The methodology includes a six-step chained/linked process consists of: (a) community data and information, (b) spatial seismic hazard analysis using next-generation attenuation, (c) interdependent community model development, (d) physical damage and functionality analysis, (e) socio-economic impact analysis and (f) structural health monitoring (SHM) and emerging technologies (ET). An illustrative case study is presented to demonstrate the seismic functionality and resilience assessment of Shelby County in Memphis, Tennessee, in the United States. From the discussion of results, it is then concluded that data from structural health monitoring and emerging technologies is a viable approach to enhance characterising the seismic hazard resilience of infrastructure, enabling rapid and in-depth understanding of structural behaviour in emergency situations. Moreover, considering the momentum of the digitalization era, setting an holistic framework on resilience that includes SHM and ET will allow reducing uncertainties that are still a challenge to quantify and propagate, supported by sequential updating techniques from Bayesian statistics.
npj Nat. Hazards
Future cities demand smart and equitable infrastructure resilience modeling perspectives
Risk-informed decisions that promote infrastructure resilience (or the ability to withstand, recover from, and adapt to stressors like natural hazards) require confident predictions of system performance now and into the future. We propose a perspective shift–one capable of handling uncertain and dynamic conditions, leveraging emerging observations from smart systems, and guided by demands for social equity. This shift requires collective efforts, but our future cities demand and deserve it.
2023
IALCCE 2023
Smart resilience: Capturing dynamic, uncertain and evolving lifecycle conditions
Raul Rincon and Jamie Ellen Padgett
In Life-Cycle of Structures and Infrastructure Systems — Proceedings of the 8th International Symposium on Life-Cycle Civil Engineering (IALCCE 2023), Nov 2023
