Multi-Hazard Reliability Analysis of Lifeline Networks

Javanbarg, Mohammad; Scawthorn, Charles; Kiyono, Junji; Ono, Yuka

doi:10.1061/41050(357)106

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…The damage caused by a tsunami could be devastating and have a significant impact on coastal infrastructure. [1][2][3][4] For instance, the 2011 Great East Japan earthquake was followed by a tsunami and caused immense economic losses, with a minimum estimate of 16.9 trillion JPY (211 billion USD as of September 2022), constituting one of the costliest natural disasters ever recorded. 5 Furthermore, the recurring Nankai-Tonankai Trough megathrust subduction has historically triggered earthquakes with M w values greater than 8.0 and generated massive tsunamis that have eradicated coastal infrastructure in the southwestern region of Japan.…”

Section: Introductionmentioning

confidence: 99%

“…The damage caused by a tsunami could be devastating and have a significant impact on coastal infrastructure 1–4 . For instance, the 2011 Great East Japan earthquake was followed by a tsunami and caused immense economic losses, with a minimum estimate of 16.9 trillion JPY (211 billion USD as of September 2022), constituting one of the costliest natural disasters ever recorded 5 .…”

Section: Introductionmentioning

confidence: 99%

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Life‐cycle risk assessment of building portfolios subjected to tsunamis under non‐stationary sea‐level rise based on a compound renewal process

Alhamid

Akiyama

Aoki

et al. 2023

Earthq Engng Struct Dyn

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The immense impacts of tsunamis can inflict substantial damage on coastal infrastructure systems during their lifetime and lead to considerable economic loss. However, with the increasing intensity and variability of sea‐level rise due to climate change, evaluations of the life‐cycle tsunami risk associated with cumulative loss have become increasingly complex since tsunami hazards are time‐dependent. In addition, the number of earthquake events and the corresponding arrival times are substantially uncertain. Therefore, an accurate life‐cycle tsunami risk assessment methodology should be established to appropriately develop disaster mitigation measures. This paper provides a novel life‐cycle risk assessment of building portfolios subjected to tsunami hazards under non‐stationary sea‐level rise effects due to climate change. The cumulative loss of a building portfolio is evaluated through a compound renewal process based on earthquake interarrival time uncertainties and time‐dependent risk. The earthquake interarrival times are modeled using a non‐Poisson process based on historical data. Tsunami hazard curves that consider the effects of sea‐level rise, estimated based on climate models, are obtained with tsunami propagation analysis. The time‐variant annual risk is estimated based on reliability and building unit loss. Finally, a numerical procedure is proposed to estimate the life‐cycle tsunami risk of building portfolios. An illustrative example is provided by applying the framework to several municipalities in the Kochi Prefecture of Japan to assess the effects of climate change on the life‐cycle tsunami risk.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Life‐cycle risk assessment of building portfolios subjected to tsunamis under non‐stationary sea‐level rise based on a compound renewal process

Alhamid

Akiyama

Aoki

et al. 2023

Earthq Engng Struct Dyn

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“…Calculating business interruption in multi-hazard scenarios is a challenge in itself. Interactions among multiple natural and technical hazards affecting a region have been extensively studied in the past (Gill and Malamud 2014;Kappes et al 2012;Marzocchi et al 2012;Javanbarg et al 2009). In the context of multi-hazard analysis, primary hazards refer to those that occur independently of other hazards, while secondary hazards, or perils, are triggered by the primary hazards.…”

Section: Introductionmentioning

confidence: 99%

Multi-peril risk assessment for business downtime of industrial facilities

Prabhu

Javanbarg

Lehmann³

et al. 2019

Nat Hazards

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The losses incurred by industrial facilities following catastrophic events can be broadly broken down into property damage and business interruption due to the ensuing downtime. This article describes a generalized probabilistic methodology for estimating facility downtime under multi-hazard scenarios. Since the vulnerability of each components of an industrial facility varies with the types of hazard, it is beneficial to adopt a system-of-systems approach for analyzing such complex facilities under multiple interdependent hazards. In this approach, the complex layout of the facility is first broken down into its constituent components. The component vulnerabilities to different hazards are combined using Boolean logic, assuming their repair time as a common basis for defining damage states of the component. This combination results in multi-hazard fragility functions for each component of the system, which give the probability of damage under combined occurrence of multiple perils. The time to repair a component is expressed probabilistically using restoration functions. Using fault tree analysis, the components' fragility functions and restoration functions are propagated to calculate system-level downtime. We demonstrate the methodology on a case-study power plant to estimate downtime risk under combined earthquake and tsunami hazard.

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Reliability and Component Importance in Networks Subject to Spatially Distributed Hazards Followed by Cascading Failures

Scherb

Garrè

Straub

2017

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

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We investigate reliability and component importance in spatially distributed infrastructure networks subject to hazards characterized by large-scale spatial dependencies. In particular, we consider a selected IEEE benchmark power transmission system. A generic hazard model is formulated through a random field with continuously scalable spatial autocorrelation to study extrinsic common-cause-failure events such as storms or earthquakes. Network performance is described by a topological model, which accounts for cascading failures due to load redistribution after initial triggering events. Network reliability is then quantified in terms of the decrease in network efficiency and number of lost lines. Selected importance measures are calculated to rank single components according to their influence on the overall system reliability. This enables the identification of network components that have the strongest effect on system reliability. We thereby propose to distinguish component importance related to initial (triggering) failures and component importance related to cascading failures. Numerical investigations are performed for varying correlation lengths of the random field to represent different hazard characteristics. Results indicate that the spatial correlation has a discernible influence on the system reliability and component importance measures, while the component rankings are only mildly affected by the spatial correlation. We also find that the proposed component importance measures provide an efficient basis for planning network improvements.

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Multi-Hazard Reliability Analysis of Lifeline Networks

Cited by 4 publications

References 6 publications

Life‐cycle risk assessment of building portfolios subjected to tsunamis under non‐stationary sea‐level rise based on a compound renewal process

Life‐cycle risk assessment of building portfolios subjected to tsunamis under non‐stationary sea‐level rise based on a compound renewal process

Multi-peril risk assessment for business downtime of industrial facilities

Reliability and Component Importance in Networks Subject to Spatially Distributed Hazards Followed by Cascading Failures

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