Identifying Early-Phase Recovery Bottlenecks Through Outcome-Based Metrics in an Integrated Regional Resilience Assessment Platform

Disaster recovery is often governed not by physical damage alone, but by the ability of communities to mobilize and allocate limited recovery resources across space and time. While recent recovery simulation approaches have demonstrated how resource and service constraints shape aggregate recovery trajectories, such “coarse-grained” metrics provide limited guidance for decision-making during the immediate aftermath of a disaster. In particular, they offer little insight into where and why recovery processes stall across localities during the critical early days following an event, when intervention priorities must be set under uncertainty.

Given a community’s damage state after a hypothetical earthquake scenario, we examine its recovery process, such as clean-up, inspection, and repair, under a suite of recovery resource allocation scenarios. The analysis uses SimCenter’s R2D tool together with embedded infrastructure system operation simulators and the recovery simulator pyrecodes. For each recovery resource allocation scenario, two complementary indicators, namely resource occupancy and first-passage unfinished metric, are introduced. Resource occupancy is defined as the fraction of components within each locality that are actively engaged in a given recovery stage at a specific time. First-passage unfinished metrics are defined as the fraction of components within each locality that are awaiting initiation of a recovery stage by a given day. When evaluated at early time horizons, these indicators reveal spatially heterogeneous recovery bottlenecks that are not apparent from system-level recovery curves.

Recognizing that the availability of recovery resources is difficult to specify before a disaster and may change as additional resources are mobilized in the immediate aftermath of an event, a sensitivity analysis may be necessary for planning optimal recovery resource allocation strategies. To facilitate sensitivity analysis within a reasonable computing time, we combine recovery simulations with a surrogate modelling approach to enable rapid recovery simulations. In particular, polynomial chaos expansion is used as a computationally efficient surrogate to relate alternative resource allocation levels to locality-level recovery indicators. This enables efficient exploration of potential allocation scenarios after a disaster occurs, without requiring repeated high-fidelity recovery simulations, and supports time-constrained decision-making in the early response phase.

By emphasizing early-phase (e.g., debris cleaning), locality-resolved diagnostics of resource bottlenecks, rather than aggregate recovery timelines, this study advances the assessment of systemic resilience in cascading risk contexts. The results demonstrate how existing recovery simulation tools, augmented with efficient surrogate modelling, can provide actionable insights for emergency managers and planners to prioritize interventions and allocate limited recovery resources across interconnected urban systems.