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.
The question
Multiscale resilience analysis stitches together sub-models for hazard, deterioration, performance, and consequences. Beyond the usual parameter uncertainty, the choices among those sub-models (for example, selecting their fidelity or the quantity of interest) introduce a subjectivity that is rarely quantified. This modeling uncertainty compounds across scales, can overshadow careful sub-model refinement, and shifts outcomes for the very populations for whom decisions are made.
What we do
We quantify the uncertainty that arises from the modeling process itself, not only from model inputs. Using statistical-distance frameworks and bias-quantification methods, we measure how modeling choices interact and compound, and we study how the resulting outcome shifts reach distinct communities. The goal is model-selection guidance that trades model efficiency against bias and against the social cost of decisions made on biased estimates, making the science of measuring resilience more objective, equitable, and fit-for-purpose.
Publications
2025
Bias quantification algorithm to measure the compounded effect of submodels’ fidelity on multiscale infrastructure performance estimates
Raul Rincon and Jamie Ellen Padgett
2025
Reliability Engineering and System Safety, in review (August 2025)
2024
Earthquake Spectra
Fragility modeling practices and their implications on risk and resilience analysis: From the structure to the network scale
Although fragility function development for structures is a mature field, it has recently thrived on new algorithms propelled by machine learning (ML) methods along with heightened emphasis on functions tailored for community- to regional-scale application. This article seeks to critically assess the implications of adopting alternative traditional and emerging fragility modeling practices within seismic risk and resilience quantification to guide future analyses that span from the structure to infrastructure network scale. For example, this article probes the similarities and differences in traditional and ML techniques for demand modeling, discusses the shift from one-parameter to multiparameter fragility models, and assesses the variations in fragility outcomes via statistical distance concepts. Moreover, the previously unexplored influence of these practices on a range of performance measures (e.g. conditional probability of damage, risk of losses to individual structures, portfolio risks, and network recovery trajectories) is systematically evaluated via the posed statistical distance metrics. To this end, case studies using bridges and transportation networks are leveraged to systematically test the implications of alternative seismic fragility modeling practices. The results show that, contrary to the classically adopted archetype fragilities, parameterized ML-based models achieve similar results on individual risk metrics compared to structure-specific fragilities, promising to improve portfolio fragility definitions, deliver satisfactory risk and resilience outcomes at different scales, and pinpoint structures whose poor performance extends to the global network resilience estimates. Using flexible fragility models to depict heterogeneous portfolios is expected to support dynamic decisions that may take place at different scales, space, and time, throughout infrastructure systems.
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.
EMI/PMC 2024
Coupling Effects of Fragility Fidelity and Network Resolution in Infrastructure Resilience
Raul Rincon and Jamie Ellen Padgett
In Engineering Mechanics Institute Conference and Probabilistic Mechanics & Reliability Conference (EMI/PMC 2024), Nov 2024
