Probabilistic Risk Assessment of Oil and Gas Infrastructures for Seismic Extreme Events

Urlainis, Alon; Shohet, Igal M.; Levy, Robert

doi:10.1016/j.proeng.2015.10.112

Cited by 23 publications

(11 citation statements)

References 17 publications

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“…The safety level of land-use planning can then be improved (Alileche et al, 2016). Probabilistic Risk Assessment (PRA) methods are also extensively used in the field of safety management and risk analysis Urlainis et al, 2015;Špačková et al, 2013). With the rapid development of information and computer technology, big data theory and advanced techniques were also introduced to study the mechanism of safety and accident.…”

Section: Introductionmentioning

confidence: 99%

Vessel traffic safety in busy waterways: A case study of accidents in western shenzhen port

Mou

Chen

et al. 2019

Accident Analysis & Prevention

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Section: Introductionmentioning

confidence: 99%

Vessel traffic safety in busy waterways: A case study of accidents in western shenzhen port

Mou

Chen

et al. 2019

Accident Analysis & Prevention

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“…The result is a higher social vulnerability in the face of loss of continuous operation (Kröger, 2008). The main objective was to improve the social preparedness for the growing landslide risk, according to the suggestions of several authors (Gene Corley et al, 1998;Baldridge and Marshall, 2011;Urlainis et al, 2014Urlainis et al, , 2015. This led to the development of several approaches and frameworks for increasing the resiliency of society exposed to the risk (Kröger, 2008;Cagno et al, 2011, and references therein).…”

Section: Introductionmentioning

confidence: 99%

Big data managing in a landslide early warning system: experience from a ground-based interferometric radar application

Intrieri

Bardi

Fanti

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. A big challenge in terms or landslide risk mitigation is represented by increasing the resiliency of society exposed to the risk. Among the possible strategies with which to reach this goal, there is the implementation of early warning systems. This paper describes a procedure to improve early warning activities in areas affected by high landslide risk, such as those classified as critical infrastructures for their central role in society.This research is part of the project "LEWIS (Landslides Early Warning Integrated System): An Integrated System for Landslide Monitoring, Early Warning and Risk Mitigation along Lifelines".LEWIS is composed of a susceptibility assessment methodology providing information for single points and areal monitoring systems, a data transmission network and a data collecting and processing center (DCPC), where readings from all monitoring systems and mathematical models converge and which sets the basis for warning and intervention activities.The aim of this paper is to show how logistic issues linked to advanced monitoring techniques, such as big data transfer and storing, can be dealt with compatibly with an early warning system. Therefore, we focus on the interaction between an areal monitoring tool (a ground-based interferometric radar) and the DCPC. By converting complex data into ASCII strings and through appropriate data cropping and average, and by implementing an algorithm for line-of-sight correction, we managed to reduce the data daily output without compromising the capability for performing.

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“…In this step, the expected damage state as a result of a seismic event is formulated in terms of fragility curve. The fragility curve expresses the probability of reaching or exceeding certain damage states for a given level of IM [13][14][15][16]. This function is fully defined by determination of two parameters: median capacity of the component to resist the damage state ( ) and standard deviation of the capacity ( ).…”

Section: System's Seismic Vulnerabilitymentioning

confidence: 99%

Probabilistic Risk Appraisal and Mitigation of Critical Infrastructures for Seismic Extreme Events

Urlainis¹,

Shohet²

2019

Proceedings of the Creative Construction Conference 2019

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The importance and the interdependencies of critical infrastructures such as power and water supply, communications, and healthcare is increasing continuously and constantly. Most of the vital services for the private and the public sectors depend on the continuous performance of critical infrastructures. However, the last decades' extreme events reveal a significant gap between the preparedness of critical infrastructures and the actual risk that those infrastructures are exposed to in case of seismic event. In this research a methodology is developed to appraise and mitigate the risk that critical infrastructures are exposed to in case of seismic events. The proposed method is designated also to act as decision support tool for the selection of the most advantageous strategy to reduce the risk expectancy for extreme seismic events. A Probabilistic Seismic Hazard Analysis (PSHA) approach is used in order to reflect a variety of possible seismic scenarios and overcome the uncertainties regarding to the timing, the location, and the magnitude of an earthquake. The seismic vulnerability of different components is evaluated by adjusted fragility curves and Fault-Tree-Analysis. The seismic risk function, that expresses the expected risk of the system for a given ground motion intensity, is derived according to the occurrence probabilities of the earthquake, the seismic vulnerability of different components, and the expected consequences. This paper introduces the developed methodology and demonstrates the key steps through two case studies of oil pumping plant and oil tank farm. The pumping plant case study demonstrates the development of the risk function and examines the contribution of a possible mitigation strategy on the overall risk expectancy. The oil tank farm case demonstrates a derivation of an exclusive fragility function for critical infrastructures facility. This methodology provides a novel analytical and decision-support tool that integrates between the components adjusted fragility curves in the risk assessment and the consequent mitigation step; the optimal mitigation strategy is derived from the fragility parameters reflection on the total risk function.

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Probabilistic Risk Assessment of Oil and Gas Infrastructures for Seismic Extreme Events

Cited by 23 publications

References 17 publications

Vessel traffic safety in busy waterways: A case study of accidents in western shenzhen port

Vessel traffic safety in busy waterways: A case study of accidents in western shenzhen port

Big data managing in a landslide early warning system: experience from a ground-based interferometric radar application

Probabilistic Risk Appraisal and Mitigation of Critical Infrastructures for Seismic Extreme Events

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