The functioning of interdependent civil infrastructure systems in the aftermath of a disruptive event is critical to the performance and vitality of any modern urban community. Post-event stressors and chaotic circumstances, time limitations, and complexities in the community recovery process highlight the necessity for a comprehensive decision-making framework at the community-level for post-event recovery management. Such a framework must be able to handle large-scale scheduling and decision processes, which involve difficult control problems with large combinatorial decision spaces. This study utilizes approximate dynamic programming algorithms along with heuristics for the identification of optimal community recovery actions following the occurrence of an extreme earthquake event. The proposed approach addresses the curse of dimensionality in its analysis and management of multi-state, large-scale infrastructure systems. Furthermore, the proposed approach can consider the cur-rent recovery policies of responsible public and private entities within the community and shows how their performance might be improved. A testbed community coarsely modeled after Gilroy, California, is utilized as an illustrative example. While the illustration provides optimal policies for the Electrical Power Network serving Gilroy following a severe earthquake, preliminary work shows that the methodology is computationally well suited to other infrastructure systems and hazards.
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A sequential decision making prospective on resilience
We investigate how sequential decision making analysis can be used for modeling system resilience. In the aftermath of an extreme event, agents involved in the emergency management aim at an optimal recovery process, trading off the loss due to lack of system functionality with the investment needed for a fast recovery. This process can be formulated as a sequential decision-making optimization problem, where the overall loss has to be minimized by adopting an appropriate policy, and dynamic programming applied to Markov Decision Processes (MDPs) provides a rational and computationally feasible framework for a quantitative analysis. The paper investigates how trends of post-event loss and recovery can be understood in light of the sequential decision making framework. Specifically, it is well known that system’s functionality is often taken to a level different from that before the event: this can be the result of budget constraints and/or economic opportunity, and the framework has the potential of integrating these considerations. But we focus on the specific case of an agent learning something new about the process, and reacting by updating the target functionality level of the system. We illustrate how this can happen in a simplified setting, by using Hidden-Model MPDs (HM-MDPs) for modelling the management of a set of components under model uncertainty. When an extreme event occurs, the agent updates the hazard model and, consequently, her response and long-term planning.
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- Award ID(s):
- 1638327
- PAR ID:
- 10065508
- Date Published:
- Journal Name:
- Safety, Reliability, Risk, Resilience and Sustainability of Structures and Infrastructure 12th Int. Conf. on Structural Safety and Reliability, Vienna, Austria, 6–10 August 2017
- Volume:
- 1
- Page Range / eLocation ID:
- 2633-2640
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The functioning of interdependent civil infrastructure systems in the aftermath of a disruptive event is critical to the performance and vitality of any modern urban community. Post-event stressors and chaotic circumstances, time limitations, and complexities in the community recovery process highlight the necessity for a comprehensive decision-making framework at the community-level for post-event recovery management. Such a framework must be able to handle large-scale scheduling and decision processes, which involve difficult control problems with large combinatorial decision spaces. This study utilizes approximate dynamic programming algorithms along with heuristics for the identification of optimal community recovery actions following the occurrence of an extreme earthquake event. The proposed approach addresses the curse of dimensionality in its analysis and management of multi-state, large-scale infrastructure systems. Furthermore, the proposed approach can consider the cur-rent recovery policies of responsible public and private entities within the community and shows how their performance might be improved. A testbed community coarsely modeled after Gilroy, California, is utilized as an illustrative example. While the illustration provides optimal policies for the Electrical Power Network serving Gilroy following a severe earthquake, preliminary work shows that the methodology is computationally well suited to other infrastructure systems and hazards.more » « less
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Accepted Manuscript1.0
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