Bayesian-based dynamic forecasting of infrastructure restoration progress following extreme events. (1st February 2023)

• Record Type: Journal Article
• Authors: Li, Yitong; Ji, Wenying
• Is Part Of: International journal of disaster risk reduction. Volume 85(2023)
• Publication Date: 2023-02-01
• Subjects: Infrastructure resilience -- Infrastructure functionality -- Power infrastructure -- Hurricane Irma -- Earned schedule -- Markov chain Monte Carlo (MCMC); Emergency management -- Periodicals; Risk management -- Periodicals; Disaster relief -- Periodicals; Hazard mitigation -- Periodicals; 363.34
• DOI: 10.1016/j.ijdrr.2022.103519
• Languages: English
• ISSNs: 2212-4209

Abstract

Abstract: Following an extreme event, efficient restoration of infrastructure functionality is a paramount task for sustaining community lifelines. With the overall goal of improving the rapidity of infrastructure restoration, the objective of this research is to forecast up-to-date infrastructure restoration progress considering associated uncertainties through integrating Bayesian inference and earned schedule. In this research, beta cumulative distribution function is assumed to represent infrastructure restoration progress. As infrastructure restoration progresses, Bayesian inference with Markov chain Monte Carlo is applied to update the planned restoration progress. Based on the updated progress, earned schedule and Monte Carlo simulation are specialized to forecast the future restoration progress as well as considering restoration-associated uncertainties. A case study on power infrastructure restoration during Hurricane Irma at Miami-Dade County was presented. The results demonstrate the updating capability of the proposed approach and underline the importance of plan updating during infrastructure restoration. In practice, a reliable and up-to-date progress forecasting better informs practitioners to understand the latest infrastructure restoration status, thereby enhancing restoration operations to impacted communities in a timely manner. Highlights: A Bayesian-based approach is derived to forecast up-to-date restoration progress considering uncertainties. The dynamic change of infrastructure restoration progress is depicted using Beta cumulative distribution function. The latest restoration progress is updated using newly collected restoration information and Markov chain Monte Carlo. Future restoration progress is forecasted using earned schedule and Monte Carlo simulation. The proposed approach enhances the monitor and control of infrastructure restoration progress.