Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article

Degree

Doctor of Philosophy

Program

Civil and Environmental Engineering

Collaborative Specialization

Hazards, Risks, and Resilience

Supervisor

Najafi, Mohammad Reza

2nd Supervisor

Goda, Katsuichiro

Co-Supervisor

3rd Supervisor

Peerhossaini, Hassan

Co-Supervisor

Abstract

The escalating frequency and intensity of natural hazards necessitate robust methodologies for assessing and enhancing the resilience of interconnected infrastructure systems. This thesis introduces a comprehensive framework for multi-hazard risk and resilience assessment within these systems, with a particular focus on the impacts of climate change. The framework underscores the potential for cascading failures, where the malfunction of one component can initiate a chain of cascading failures throughout the network, resulting in widespread economic disruption and significant societal consequences. The first study proposes an integrated framework, BN-SPM, based on Bayesian Network (BN) and Strongest Path Method (SPM) to assess natural hazard risks to interconnected infrastructure systems. By leveraging Bayesian inference, the BN transforms prior failure probabilities into posterior ones, while SPM prioritizes network entities based on impact, vulnerability, and risk indices. This model evaluates various scenarios, including non-hazard conditions, flooding hazards, and mitigation strategies. The results highlight and emphasize the need for robust risk assessment in interconnected systems. The second study addresses the limitations of static risk assessments by introducing a Dynamic Bayesian Network (DBN) framework for evaluating the dynamic behavior of interconnected infrastructure systems under multi-hazard scenarios. This study investigates the complex network of critical infrastructure, focusing on the probabilistic conditions and functional dynamics of disruptions and restoration processes. The results reveal and underscore the necessity of dynamic risk assessments for multi-hazard scenarios to develop robust risk management strategies. The third study advances the assessment methodology by integrating Fuzzy Dynamic Bayesian Networks (FDBNs) with auxiliary nodes, aiming to reduce computational complexity while modeling uncertainties and temporal dynamics in hazard impacts and recovery processes. Two scenarios are investigated: multi-hazard events involving flooding and landslides, and climate change-induced flooding. The findings highlight differential impacts on infrastructure resilience, according to the crucial role of some entities rather than others. The integration of FDBNs with auxiliary nodes improves model accuracy and reduces computational complexity, providing strategic insights for resilient infrastructure planning. The implications of the abovementioned phases of this study are evaluated on Saint Lucia’s vulnerable southern coast, including Hewanorra International Airport (HIA). Overall, this thesis advances methodologies for resilient infrastructure planning and management, addressing critical gaps in current approaches. The findings emphasize strategic planning and adaptive resilience-building measures to enhance infrastructure robustness and minimize socio-economic disruptions in hazard-prone regions.

Summary for Lay Audience

In today’s world, the increasing frequency and severity of natural hazards highlight the urgent need for better ways to assess and strengthen infrastructures against such threats. This thesis delves into innovative approaches aimed at enhancing the resilience of interconnected infrastructure systems, focusing on Saint Lucia’s vulnerable southern coast, particularly Hewanorra International Airport (HIA). Saint Lucia is a volcanic island with a mountainous landscape that is mostly confined to coastal lowlands and located in the tropical cyclone belt. The first study introduces a method, combining different techniques to evaluate the risks posed by natural hazards to interconnected infrastructures. By refining initial assessments of failure probabilities, this method identifies critical network components based on their potential impact and vulnerability. This study underscores HIA’s vulnerability, highlighting the importance of robust risk assessments in safeguarding complex infrastructure networks. The second study advances beyond traditional risk assessments by using simulations to model dynamic interactions within infrastructure systems during hazard scenarios. By simulating various failure scenarios, including floods and landslides, as multi-hazard, the framework reveals vulnerabilities in both the airport and tourism sectors, emphasizing the need for dynamic risk assessments to develop adaptive strategies for managing multi-hazard scenarios effectively. In the third study, a novel approach using advanced modeling techniques is introduced. This method reduces computational complexity while accounting for uncertainties and temporal dynamics in hazard impacts and recovery processes. Studying scenarios involving floods, landslides, and climate change impacts, the research identifies critical vulnerabilities in infrastructure components crucial to the regional economy, such as transportation infrastructure. Overall, this thesis contributes valuable insights into resilient infrastructure planning and management. By bridging gaps in current methodologies, the studies emphasize strategic planning and adaptive measures to bolster infrastructure robustness and minimize socio-economic disruptions in hazard-prone regions. This research is pivotal in ensuring that infrastructure can withstand and recover from the growing challenges posed by natural disasters, safeguarding communities and economies alike.

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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