A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item Application of Bayesian Networks to Assess the Seismic Hazard for an Infrastructure System in the Central and Eastern United States(2024) Gibson, Emily M.; Bensi, Michelle T; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Earthquakes are often considered by the public to be a hazard restricted to the Western United States (WUS). However, earthquakes occur in the Central and Eastern United States (CEUS) as observed with the August 23, 2011 Mineral, Virginia earthquake, which damaged approximately 600 residential properties, resulted in 200-300 million dollars in economic losses, and initiated a shutdown of the North Anna nuclear power plant (Horton et al., 2015). Since earthquakes occur more frequently in the WUS, most seismic research is performed to support the WUS tectonic regime. This is also true when developing methods to assess the seismic risk to infrastructure systems, and current practices involve performing a large number of simulations of potential earthquake events, ground motion fields, structural performance, and failure consequences. These simulations can require significant computational resources, and it may be difficult to convince stakeholders to assess the seismic risk of their infrastructure system in the CEUS since earthquakes occur less often and perceived risks are lower. However, this risk must be assessed, given the density and age of infrastructure in the CEUS. Additionally, ground motion attenuation is lower in the region, so infrastructure distributed across greater distances may be impacted during an earthquake event. As a first step in developing a method that is tailored to assess system risk in the CEUS, this research proposes a Bayesian Network (BN) framework to estimate multi-site seismic hazards. Importantly, this framework utilizes existing products from a Probabilistic Seismic Hazard Analysis (PSHA), which reduces computational burdens and allows a user to incorporate the epistemic uncertainty characterized by experts as part of previously performed large-resource efforts. Additionally, the framework incorporates sources of hazard correlation between sites in a transparent and computationally tractable manner. An example problem is provided to validate this framework against a simulation that reflects the current state of practice in the WUS. Applications of the framework are then explored to assess when various input parameters may influence hazard results and identify when more or less resource-intensive assessments may be appropriate. This includes evaluating the impact of the ground motion within-event residual correlation and site separation distance. A scenario is also presented to illustrate how the BN can be used to make hazard-informed decisions in the context of the operation of two dams. The framework is then expanded to illustrate how failure modes can be characterized to understand system performance better. Since hazard correlation is an important aspect of the multi-site hazard, within-event residual correlation in the CEUS is also investigated. Empirical models are available to estimate ground motion within-event residual correlation in the WUS, but these may not be appropriate for the CEUS, given the lower attenuation. Earthquake recordings available from the NGAEast database (Goulet et al., 2014) and applicable CEUS ground motion models are used to calculate ground motion residuals. Correlation between the residuals at different sites is analyzed and compared against models developed for the WUS. Insights from this analysis and the proposed framework are provided to aid practitioners in assessing seismic risk for an infrastructure system in the CEUS.Item Exploiting Causal Reasoning to Improve the Quantitative Risk Assessment of Engineering Systems Through Interventions and Counterfactuals(2023) Ruiz-Tagle, Andres; Groth, Katrina; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The main strength of quantitative risk assessment (QRA) is to enable risk management by providing causal insights into the risk of an engineering system or process. Bayesian Networks (BNs) have become popular in QRA because they offer an improved causal structure that represents analysts’ knowledge of a system and enable reasoning under uncertainty. Currently, the use of BNs for risk-informed decisions is based solely on associative reasoning, answering questions of the form "If we observe X=x, how likely is it to observe Y=y?” However, risk management in the industry relies on understanding how a system could change in response to external influences (e.g., interventions and decisions) and identifying the causes and mechanisms that could explain the outcome of past events (e.g., accident investigations and lessons learned). This dissertation shows that associative reasoning alone is insufficient to provide these insights, and it provides a framework for obtaining more complex causal insight using BNs with intervention and counterfactual reasoning. Intervention and counterfactual reasoning must be implemented along with BNs to provide more complex insights about the risk of a system. Intervention reasoning answers queries of the form "How does doing X=x change the likelihood of observing Y=y?” and can be used to inform the causal effect of interventions and decisions on the risk and reliability of a system. Counterfactual reasoning answers queries of the form "Had X been X=x' in an event, instead of the observed X=x, could Y have been Y=y', instead of the observed Y=y?” and can be used to learn from past events and improve safety management activities. BNs present a unique opportunity as a risk modeling approach that incorporates the complex causal dependencies present in a system’s variables and allows reasoning under uncertainty. Therefore, exploiting the causal reasoning capabilities of BNs in QRAs can be highly beneficial to improve modern risk analysis. The goal of this work is to define how to exploit the causal reasoning capabilities of BNs to support intervention and counterfactual reasoning in the QRA of complex systems and processes. To achieve this goal, this research first establishes the mathematical background and methods required to model interventions and counterfactuals within a BN approach. Then, we demonstrate the proposed methods with two case studies concerning the risk of third-party excavation damage to natural gas pipelines in the U.S. The first case study showed that the intervention reasoning methods developed in this work produce unbiased causal insights into the effectiveness of implementing new excavation practices. The second case study showed how the counterfactual reasoning methods developed in this work can expand on the lessons learned from an accident investigation on the Sun Prairie 2018 gas explosion by providing new insights into the effectiveness of current damage prevention practices. Finally, associative, intervention, and counterfactual reasoning methods with BNs were integrated into a single model and used to assess the risk of a highly complex challenge for the future of clean energy: excavation damages to natural gas pipelines transporting hydrogen. The impact of this research is a first-of-its-kind approach and a novel set of QRA methods that provide expanded causal insights for understanding failures and accidents in complex engineering systems and processes.Item A BAYESIAN NETWORK PERSPECTIVE ON THE ELEMENTS OF A NUCLEAR POWER PLANT MULTI-UNIT SEISMIC PROBABILISTIC RISK ASSESSMENT(2021) DeJesus Segarra, Jonathan; Bensi, Michelle T.; Modarres, Mohammad; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Nuclear power plants (NPPs) generated about 10% of the world’s electricity in 2020 and about 1/3 of the world’s low-carbon electricity production. Probabilistic risk assessments (PRAs) are used to estimate the risk posed by NPPs, generate insights related to strengths and vulnerabilities, and support risk-informed decisionmaking related to safety and reliability. While PRAs are typically carried out on a reactor-by-reactor basis, the Fukushima Dai-ichi accident highlighted the need to also consider multi-unit accidents. To properly characterize the risks of reactor core damage and subsequent radiation release at a multi-unit site, it is necessary to account for dependencies among reactors arising from the possibility that adverse conditions affect multiple units concurrently. For instance, the seismic hazard is one of the most critical threats to NPP structures, systems, and components (SSCs) because it affects their redundancy. Seismic PRAs are comprised of three elements: seismic hazard analysis, fragility evaluation, and systems analysis. This dissertation presents a Bayesian network (BN) perspective on the elements of a multi-unit seismic PRA (MUSPRA) by outlining a MUSPRA approach that accounts for the dependencies across NPP reactor units. BNs offer the following advantages: graphical representation that enables transparency and facilitates communicating modeling assumptions; efficiency in modeling complex dependencies; ability to accommodate differing probability distribution assumptions; and facilitating multi-directional inference, which allows for the efficient calculation of joint and conditional probability distributions for all random variables in the BN. The proposed MUSPRA approach considers the spatial variability of the ground motions (hazard analysis), dependent seismic performance of SSCs (fragility evaluation), and efficient BN modeling of systems (systems analysis). Considering the spatial variability of ground motions represents an improvement over the typical assumption that ground motions across a NPP site are perfectly correlated. The method to model dependent seismic performance of SSCs presented is an improvement over the current “perfectly dependent or independent” approach for dependent seismic performance and provides system failure probability results that comply with theoretical bounds. Accounting for these dependencies in a systematic manner makes the MUSPRA more realistic and, therefore, should provide confidence in its results (calculated metrics) and risk insights.