Seismic risk and resilience analysis of networked industrial facilities

Industrial facilities, as an essential part of socio-economic systems, are susceptible to disruptions caused by earthquakes. Such disruptions may result from direct structural damage to facilities or their loss of functionality due to impacts on their support facilities and infrastructure systems. Decisions to improve the seismic performance of industrial facilities should ideally be informed by risk (and resilience) analysis, taking into account their loss of functionality and the following recovery under the influence of various sources of uncertainty. Rather than targeting specific individual facilities like a hazardous chemical plant, our objective is to quantify the resilience of interacting industrial facilities (i.e., networked industrial facilities) in the face of uncertain seismic events while accounting for their functional dependencies on infrastructure systems. A specific facility, such as a hazardous chemical plant, can be a compound node in the network representation, interacting with other facilities and their supporting infrastructure components. In this context, a compound node is a complex system in its own right. To this end, this paper proposes a formulation to model the functionality of interacting industrial facilities and infrastructure using a system of coupled differential equations, representing dynamic processes on interdependent networked systems. The equations are subject to uncertain initial conditions and have uncertain coefficients, capturing the effects of uncertainties in earthquake intensity measures, structural damage, and post-disaster recovery process. The paper presents a computationally tractable approach to quantify and propagate various sources of uncertainty through the formulated equations. It also discusses the recovery of damaged industrial facilities and infrastructure components under resource and implementation constraints. The effects of changes in structural properties and networks’ connectivity are incorporated into the governing equations to model networks’ functionality recovery and quantify their resilience. The paper illustrates the proposed approach for the seismic resilience analysis of a hypothetical but realistic shipping company in the city of Memphis in Tennessee, United States. The example models the effects of dependent water and power infrastructure systems on the functionality disruption and recovery of networked industrial facilities subject to seismic hazards.