A Markov framework for generalized post-event systems recovery modeling: From single to multihazards
Abstract
State-dependent models can be used to represent the system recovery process as a series of stochastic transitions from lower to higher functional states. However, the applications of these models have been limited in scope and there is a lack of a generalized recovery modeling framework. A generalized framework would permit a robust forecasting of systems and system-of-systems recovery under multiple hazards, and more broadly, would contribute to community disaster preparedness. This paper develops a generalized post hazard-event recovery modeling framework based on state-dependent Markov-type processes. We then apply the proposed framework to solve a spectrum of problems that range from hind-casting single-system recovery following a single hazard event to forecasting post-event trajectories under multiple hazards and modeling the recovery of a system-of-systems. First, Markov chains are used to hind-cast the observed recovery for a portfolio of buildings affected by the 2014 South Napa, California, earthquake. Next, Markov processes are used to formulate a parametric post hazard-event recovery model, which can be updated using Bayesian statistics when relevant datasets become available. Semi-Markov processes are then used to develop a more general model of single hazard recovery, which accounts for the intensity of the loading and level of damage caused by themore »
- Authors:
-
- Idaho National Lab. (INL), Idaho Falls, ID (United States)
- Univ. of California, Los Angeles, CA (United States)
- Vanderbilt Univ., Nashville, TN (United States)
- Publication Date:
- Research Org.:
- Idaho National Lab. (INL), Idaho Falls, ID (United States)
- Sponsoring Org.:
- USDOE Office of Environment, Health, Safety and Security (AU), Office of Health and Safety
- OSTI Identifier:
- 1778696
- Report Number(s):
- INL/JOU-19-55704-Rev000
Journal ID: ISSN 0167-4730; TRN: US2209563
- Grant/Contract Number:
- AC07-05ID14517
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Structural Safety
- Additional Journal Information:
- Journal Volume: 91; Journal ID: ISSN 0167-4730
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 42 ENGINEERING; 99 GENERAL AND MISCELLANEOUS; Markov models; Post hazard-event infrastructure recovery; Infrastructure resilience; Natural hazards; Multihazard assessment; Bayesian Networks
Citation Formats
Dhulipala, Somayajulu L.N., Burton, Henry V., and Baroud, Hiba. A Markov framework for generalized post-event systems recovery modeling: From single to multihazards. United States: N. p., 2021.
Web. doi:10.1016/j.strusafe.2021.102091.
Dhulipala, Somayajulu L.N., Burton, Henry V., & Baroud, Hiba. A Markov framework for generalized post-event systems recovery modeling: From single to multihazards. United States. https://doi.org/10.1016/j.strusafe.2021.102091
Dhulipala, Somayajulu L.N., Burton, Henry V., and Baroud, Hiba. Wed .
"A Markov framework for generalized post-event systems recovery modeling: From single to multihazards". United States. https://doi.org/10.1016/j.strusafe.2021.102091. https://www.osti.gov/servlets/purl/1778696.
@article{osti_1778696,
title = {A Markov framework for generalized post-event systems recovery modeling: From single to multihazards},
author = {Dhulipala, Somayajulu L.N. and Burton, Henry V. and Baroud, Hiba},
abstractNote = {State-dependent models can be used to represent the system recovery process as a series of stochastic transitions from lower to higher functional states. However, the applications of these models have been limited in scope and there is a lack of a generalized recovery modeling framework. A generalized framework would permit a robust forecasting of systems and system-of-systems recovery under multiple hazards, and more broadly, would contribute to community disaster preparedness. This paper develops a generalized post hazard-event recovery modeling framework based on state-dependent Markov-type processes. We then apply the proposed framework to solve a spectrum of problems that range from hind-casting single-system recovery following a single hazard event to forecasting post-event trajectories under multiple hazards and modeling the recovery of a system-of-systems. First, Markov chains are used to hind-cast the observed recovery for a portfolio of buildings affected by the 2014 South Napa, California, earthquake. Next, Markov processes are used to formulate a parametric post hazard-event recovery model, which can be updated using Bayesian statistics when relevant datasets become available. Semi-Markov processes are then used to develop a more general model of single hazard recovery, which accounts for the intensity of the loading and level of damage caused by the event. Semi-Markov processes with non-renewal features are then used to account for multihazard interactions in a post-event recovery model, and applied to a case study that involves a community in Charleston, South Carolina. Lastly, Markov-type processes are combined with Bayesian networks to model the recovery of residential, commercial, educational, and industrial buildings (system-of-systems) following a hazard event. Overall, these applications demonstrate the versatility of the Markov framework towards handling recovery problems with varying levels of complexity.},
doi = {10.1016/j.strusafe.2021.102091},
journal = {Structural Safety},
number = ,
volume = 91,
place = {United States},
year = {Wed Apr 14 00:00:00 EDT 2021},
month = {Wed Apr 14 00:00:00 EDT 2021}
}
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