Water and wastewater network, electric power network, transportation network, communication network, and information technology network are among the critical infrastructure in our communities; their disruption during and after hazard events greatly affects communities’ well-being, economic security, social welfare, and public health. In addition, a disruption in one network may cause disruption to other networks and lead to their reduced functionality. This paper presents a unified theoretical methodology for the modeling of dependent/interdependent infrastructure networks and incorporates it in a six-step probabilistic procedure to assess their resilience. Both the methodology and the procedure are general, can be applied to any infrastructure network and hazard, and can model different types of dependencies between networks. As an illustration, the paper models the direct effects of seismic events on the functionality of a potable water distribution network and the cascading effects of the damage of the electric power network (EPN) on the potable water distribution network (WN). The results quantify the loss of functionality and delay in the recovery process due to dependency of the WN on the EPN. The results show the importance of capturing the dependency between networks in modeling the resilience of critical infrastructure.

This paper presents a methodology that generates and links high-resolution spatial data on households and housing units with heterogeneous characteristics (i.e., size, tenure status, occupied, and vacant) to residential buildings which in turn are linked to critical infrastructure. The methodology utilizes areal demographic data from the US Census, which are probabilistically linked to an inventory of housing units located in residential buildings. By allocating high-resolution household socio-demographic data to housing units in single and multi-family residential structures themselves linked to critical infrastructure systems, coupled engineering-social science modeling is possible. This paper presents a workflow for linking social science and engineering data to enable integrated models for community resilience. The methodology is applied to Seaside, Oregon, a coastal community with a year-round population of over 6,000 persons. The application highlights the benefits of integrating social science and engineering data. Benefits include facilitating coupled modeling, accounting for uncertainty, visualization, and spatial exploration of modeled results.

Between September 2010 and June 2011 the Canterbury area experienced three major earthquakes with M …