Recovery Optimization of Interdependent Infrastructure: A Multi-Scale Approach

2) modeling the associated service recoveryand 3) developing a computationally manageable approach for the recovery modeling and optimization. This paper presents a novel multi-scale approach for the post-disaster recovery modeling and optimization of interdependent infrastructure. The multi-scale approach facilitates the recovery modeling and enables developing recovery strategies that are feasible to implement and easy to communicate. To enhance regional resilience, the paper integrates the recovery modeling into a multi-objective optimization problem. The optimization problem aims to schedule the required recovery activities such that disrupted services are restored as fast as possible, while minimizing the incurred cost. In the optimization problem, resilience metrics are introduced to monitor and quantify service recovery. The optimization problem is subject to recovery scheduling and network flow constraints, where each is formulated as a nested optimization. The multi-scale approach to the recovery optimization highlights the role of infrastructure at multiple scales to achieve selected recovery objective(s). As an illustration, the proposed approach is used to optimize the post-disaster recovery of interdependent infrastructure in a virtual community testbed.Rapid post-disaster recovery of infrastructure is necessary for prompt societal recovery. Regional resilience analysis can promote mitigation and recovery strategies that reduce the spatial extent and duration of infrastructure disruptions. Three significant challenges in regional resilience analysis are 1) modeling the physical recovery of infrastructureThis work was supported in part by the National Institute of Standards and Technology (NIST) through the Center for Risk-based Community Resilience Planning under Award No. 70NANB15H044 and by the National Science Foundation (NSF) under Award No. 1638346. Opinions and findings presented are those of the authors and do not necessarily reflect the views of the sponsors

Modeling the Joint Probability Distribution of Main Shock and Aftershock Spectral Accelerations

Seismic risk analysis of deteriorating structures and infrastructure often requires predicting the intensity measures of earthquake ground motions in main shock-aftershock sequences. The uncertainty in the intensity measures of ground motions is typically a dominant contributor to the total uncertainty of the seismic risk analysis. A model for the joint probability distribution of main shock and aftershock intensity measures is thus required to accurately quantify the uncertainty in the seismic risk analysis. The spectral accelerations of ground motions have been identified as significant intensity measures for the seismic risk analysis of structures and infrastructure. The values of spectral accelerations can be affected by many factors representing the characteristics of the seismic source, travel path of seismic waves, and local site conditions. These factors can also introduce statistical dependence among main shock and aftershock spectral accelerations. This paper develops a novel formulation for the joint probability distribution of main shock and aftershock spectral accelerations at multiple periods. We select existing predictive models for the spectral accelerations of main shocks and develop a separate model for the spectral accelerations of aftershocks. The proposed formulation also estimates the correlations between the relevant pairs of model error terms in the two probabilistic predictive models for a wide range of periods. This allows us to separately capture the similarity in source and site and thus present the physical meanings. The increased vulnerability of structures and infrastructure in the aftermath of a damaging mainshock can further highlight the significance of capturing such correlations in the seismic risk analysis