# Infrastructure Recovery Curve Estimation Using Gaussian Process Regression on Expert Elicited Data

arXiv: https://arxiv.org/abs/2008.10211 · HTML: https://mlsanity.com/paper/2008.10211/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2020.

## Built on by (most cited first, 2 of 2)

- [Deep Adaptive Arbitrary Polynomial Chaos Expansion: A Mini-data-driven Semi-supervised Method for Uncertainty Quantification](https://mlsanity.com/paper/2107.10428/) (2021)
- [Physics-Informed Deep Monte Carlo Quantile Regression method for Interval Multilevel Bayesian Network-based Satellite Heat Reliability Analysis](https://mlsanity.com/paper/2202.06860/) (2022)

## Abstract (arXiv)

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.