Research
Computational and probabilistic methods for resilient, adaptive, and equitable infrastructure systems.
Infrastructure systems are complex, multiscale, and adaptive. Their components aggregate nonlinearly into network-level outcomes, they operate under evolving hazard and societal conditions, and they are embedded in communities whose existing inequities can be amplified or mitigated by the engineering decisions made around them. The Rincon Research Group investigates computational algorithms, statistical tools, probabilistic frameworks, and decision-theoretic approaches for modeling, designing, and monitoring civil infrastructure systems exposed to natural hazards and deterioration, and, as a future direction, to climate stressors, with a commitment to making those tools accurate, transparent, and equitable.
Our work spans structural reliability and risk modeling, infrastructure network analysis, machine learning applied to natural hazards and infrastructure-engineering contexts, graph-theoretic representations of socio-infrastructure systems, uncertainty quantification, and the relationship between modeling choices and social outcomes.
The work is organized around three interconnected research thrusts.
Multiscale Infrastructure Systems Modeling
We ask how to make structure-to-network scale reliability, risk, and resilience assessments computationally feasible without sacrificing the realism of the individual submodels involved in the analysis.
Uncertainty Quantification and Uncertainties in the Modeling Process
Quantifying not just parameter uncertainty but the hidden uncertainty of modeling choices, and how it compounds across scales and communities.
Dynamic User-Physical Infrastructure Systems
How users, owners, physical networks, and environmental stressors interact and evolve. These interactions and their hidden relationships shape long-term resilience and sustainability.
Interested in joining the group or collaborating? Visit the Join page.