Two abstracts have been accepted for presentation at the 2016 Probabilistic Mechanics & Reliability Conference to be held at Vanderbilt University May 22-25, 2016 in Nashville. This work is a collaborative effort of Weili Zhang, Naiyu Wang, and Charles Nicholson.
Resilience-Based Risk Mitigation and Recovery for Highway Transportation Networks
The resilience of robust, large-scale, interdependent civil infrastructure networks plays a major role in determining the resilience of a community as a whole. The performance of transportation networks, in particular, is critical since post-disaster emergency response, recovery and restoration of virtually all other facilities and lifeline systems are dependent on people and equipment being able to move to the sites where damage has occurred. Highway bridges are points of vulnerability in transportation networks exposed to extreme natural hazards. To enhance community resilience, risk mitigation strategies and decision frameworks for transportation networks should take a system perspective at the community or regional scale and should be aimed at maximizing the resilience of the network as a whole.
In this study, we utilize modern network theory to introduce a novel, comprehensive indicator to measure resilience of a transportation network, which permits risk mitigation alternatives for improving transportation network resilience to be compared on a common basis. This metric integrates the topology, redundancy, traffic patterns, and functionality as well as structural reliability (failure probability) of individual components for network resilience quantification. A project ranking mechanism is proposed, based on the newly developed metric, for identifying and prioritizing retrofit projects that are critical for effective pre-disaster risk mitigation for bridge networks. We further propose a restoration scheduling method for optimal post-disaster recovery planning using a two-dimensional network recovery metric defined for the first time in this study to capture the characteristics of the recovery trajectory that relate to the efficacy of the restoration strategies. An illustration of this resilience-based risk mitigation and recovery framework is given using a hypothetical bridge network susceptible to seismic hazards. A sensitivity study using this network illustrates the impact of the resourcefulness of a community and its time-dependent commitment of resources on the network recovery characteristics.
Cascading Failures in Interdependent Networks: A Network Flow Approach
Modern societies depend on large-scale, interdependent networks, including transportation networks, utility networks, and telecommunication networks. Each individual network plays its own critical role in community resilience. However, these networks are not independent, but rather coupled and interdependent in a variety of ways. The fundamental characteristic of which is that the disruption of a single component could result in wide-reaching cascading failures of components in other networks. Recent extreme hazard events have shown that the initial failures of small fraction of nodes in one network may propagate to the entire interdependent system
in a catastrophic manner. Consequently, in order to realistically quantify the impact of a disaster on infrastructure systems, we need an efficient and practical model to simulate and predict the failure cascading mechanism in interdependent networks.
This study introduces a newly developed multi-level interdependent network (MLIN) modeling approach. The MLIN is a binary mixed integer programming problem and can be solved exactly by branch and bound algorithm. The output of the MLIN includes post-disaster serviceability of each network, and failure status of nodes and arcs of all networks. Furthermore, community building inventory can be integrated in the MLIN as a special layer. The MLIN supports physics-based community resilience modeling, enables strategy optimization for risk mitigation and recovery at network component level, and facilitates community resilience modeling of different resolutions.