Modeling Time-varying Reliability and Resilience of Deteriorating Infrastructure

The spatial and temporal extent of disruptions to services provided by infrastructure following disruptive events is directly related to the instantaneous state of the infrastructure and their post-disruption recovery. This paper develops a novel formulation to model the effects of infrastructure deterioration on their time-varying ability to recover after disruptive events. By unifying available models for deterioration and recovery, the paper proposes a general formulation to model the physical state and functionality of deteriorating infrastructure throughout its service life. The paper further develops resilience measures to quantify the temporal and spatial variations of infrastructure's ability to recover after disruptive events. The proposed formulation has a hierarchical structure that enables exploiting readily available data at the lower level of hierarchy to improve the prediction capability of models at the infrastructure level. Incorporating the governing physical laws in the proposed formulation also enables customizing the models to emulate the reality of infrastructure deterioration and recovery. While the formulation is general, the emphasis is on modeling potable water infrastructure as a case in which the deterioration of pipelines grows mostly undetectable until extensively developed. By the time the deterioration becomes visible, a substantial portion of the infrastructure service life has already been depleted, and costly repair or replacement would be inevitable. To illustrate, the proposed formulation has been implemented to model the time-varying reliability and resilience of the potable water infrastructure of the city of Seaside in Oregon, United States. The example highlights the effects of spatially varying exposure conditions and pipelines' age on the reliability, functionality, recovery, and resilience of the potable water infrastructure.

Automated summary

This paper introduces a novel formulation to model the effects of infrastructure deterioration on recovery after disruptive events, as well as resilience measures to quantify infrastructure's ability to recover over time. The formulation has a hierarchical structure to improve prediction capability and can be customized to emulate reality.