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Vishnupriya Jonnalagadda - Dissertation 24.08 MB
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Dissertation
ADVANCES IN UNCERTAINTY ANALYSIS FOR INFRASTRUCTURE RISK AND RESILIENCE ASSESSMENT
Washington State University
Doctor of Philosophy (PhD), Washington State University
05/2025
DOI:
https://doi.org/10.7273/000007395
Abstract
The increasing frequency and intensity of natural hazards, combined with evolving environmental stressors and material degradation, pose significant challenges to the long-term resilience of civil infrastructure systems. Effective risk and resilience assessment is complicated by multiple sources of uncertainty, including incomplete or limited knowledge of future conditions, the high computational demands of probabilistic modeling, and the underutilization of real-time monitoring data. This dissertation addresses these challenges by proposing various solutions, including robust decision-making under deep uncertainty, computationally efficient scenario reduction, and dynamic resilience assessment through real-time data integration.
The first component introduces a robustness-based material selection method using Information Gap Decision Theory (IGDT), enabling engineers to identify reinforced concrete materials capable of maintaining fire performance under deeply uncertain future scenarios. A dynamic adaptive policy pathway (DAPP) approach is then applied to develop flexible maintenance strategies that evolve over time in response to changing system conditions. The second component focuses on improving the computational feasibility of probabilistic risk assessments. A novel hybrid method combining clustering and active learning is proposed to reduce large-scale hazard scenario datasets while preserving critical information for accurate risk estimation. The third component develops a dynamic Bayesian network (DBN) model to assess the time-dependent resilience of highway networks. By integrating monitoring data, such as chloride-induced corrosion and traffic demand, into the resilience model, the DBN framework enables continuous updates to system reliability and supports adaptive infrastructure management.
Together, these components offer a cohesive framework for infrastructure decision-making that is robust, adaptive, and computationally efficient. The proposed methodologies provide practical tools for engineers, planners, and policymakers to enhance the resilience of critical infrastructure systems in the face of growing uncertainty.
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Details
- Title
- ADVANCES IN UNCERTAINTY ANALYSIS FOR INFRASTRUCTURE RISK AND RESILIENCE ASSESSMENT
- Creators
- Vishnupriya Jonnalagadda
- Contributors
- Ji Yun Lee (Chair)Xianming Shi (Committee Member)Tim Ginn (Committee Member)Abhishek Kaul (Committee Member)Yue Li (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- Department of Civil and Environmental Engineering
- Theses and Dissertations
- Doctor of Philosophy (PhD), Washington State University
- Publisher
- Washington State University
- Number of pages
- 174
- Identifiers
- 99901221253101842
- Language
- English
- Resource Type
- Dissertation