Rincon Research Group

Performance assessment of networked systems exposed to seismic events commonly uses aggregated metrics of performance, such as connectivity loss or infrastructure serviceability ratios. While global metrics like these offer a way to inform decisionmaking, understanding who is affected by the potential loss of performance and how performance varies across users can be very valuable for mitigating uneven impacts. In this paper, we adapt the attributed network representation to estimate the impact of earthquakes on network users disaggregated by their attributes. Herein, we propose the attributed-graph relative network efficiency, a connectivity-based metric that accounts for the increase in the cost of reachability between diverse user groups when the network they rely on is exposed to hazards. Using this new metric and the recently proposed attributed network connectivity loss metric, we demonstrate the unequal impact of earthquakes on diverse users in simulated networks. Additionally, we expose the impacts of network model fidelity on the estimation of global users’ performance. We leverage hypothetical origin-destination networks and seismically vulnerable bridges for illustrative purposes. The estimation of disaggregated network impacts due to earthquake hazards is expected to support more equitable decision-making when designing risk mitigation interventions, emergency response plans, and recovery management.

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.

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.