  Infrastructure recovery time estimation is critical to disaster management
and planning. Inspired by recent resilience planning initiatives, we consider a
situation where experts are asked to estimate the time for different
infrastructure systems to recover to certain functionality levels after a
scenario hazard event. We propose a methodological framework to use
expert-elicited data to estimate the expected recovery time curve of a
particular infrastructure system. This framework uses the Gaussian process
regression (GPR) to capture the experts' estimation-uncertainty and satisfy
known physical constraints of recovery processes. The framework is designed to
find a balance between the data collection cost of expert elicitation and the
prediction accuracy of GPR. We evaluate the framework on realistically
simulated expert-elicited data concerning the two case study events, the 1995
Great Hanshin-Awaji Earthquake and the 2011 Great East Japan Earthquake.
