Uncertainty Quantification and Risk Assessment Methods for Complex Systems subject to Natural Hazards

Tolo, S ORCID: 0000-0003-3547-7012
(2016) Uncertainty Quantification and Risk Assessment Methods for Complex Systems subject to Natural Hazards. PhD thesis, University of Liverpool.

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The interaction between natural events and technological installations involves complex mechanisms which have the potential to affect simultaneously more critical systems, nullifying the redundancy measures common to industrial safety systems and endangering the integrity of facilities. The concerns related to this kind of events are far from being restricted to a merely economic or industrial nature. On the contrary, due to the sensitivity of most processes performed in industrial plants and the negative consequences of eventual releases of hazardous materials, the impact of simultaneous failures embraces also the environment and population surrounding the installations. The risk is further widened by the trend of climate extremes: both observations over the past century and projections for next decades suggest an increase of the severity of extreme weather events and their frequency, both on local and global scales. The rise of sea water levels together with the exacerbation of extreme winds and precipitations, enlarge the geographic area of risk and rise the likelihood of accidents in regions historically susceptible to natural hazards. The prevention of technological accidents triggered by natural hazards lies unavoidably with the development of efficient theoretical and computational tools for the vulnerability assessment of industrial installations and the identification of effective strategies to tackle the growing risks to which they are subject. In spite of the increasing trend of the risk and the high-impact consequences, the current scientific literature still lacks robust means to tackle these issues effectively. The research presented in this dissertation addresses the critical need for novel theoretical and computational methods tailored for the risk assessment of complex systems threaten by extreme natural events. The specific requirements associated with the modelling of the interaction between external hazards and engineering systems have been determined, resulting in the identification of two main bottlenecks. On the one hand, this kind of analysis has to deal with the difficulty of representing accurately the complexity of technological systems and the mutual influence among their subsystems. On the other, the high degree of uncertainty affecting climate variables (due to their inner aleatory nature and the restricted information generally available), strongly bounds the accuracy and credibility of the results on which risk-informed decisions must be made. In this work, well-known traditional approaches (such as Bayesian Networks, Monte Carlo methods etc.) as well as cutting-edge methods from different sectors of the scientific literature have been adopted and integrated in order to obtain a novel theoretical strategy and computational tool able to overcome the limitations of the current state of the art. The result of the research is a complete tool for risk assessment and decision making support, based on the use of probabilistic graphical models and able to fully represent a wide spectrum of variables types and their uncertainty, to provide the implementation of flexible computational models as well as their computation and uncertainty quantification.

Item Type: Thesis (PhD)
Divisions: Faculty of Science and Engineering > School of Engineering
Depositing User: Symplectic Admin
Date Deposited: 31 Aug 2017 09:00
Last Modified: 19 Jan 2023 07:20
DOI: 10.17638/03005411
URI: https://livrepository.liverpool.ac.uk/id/eprint/3005411