Data-Driven Risk Assessment of Bridges Subject to Corrosion and Scour
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Aging of bridge structural components due to natural degradation events, such as corrosion and scour, creates safety issues in the structural system and can lead to possible bridge failures. Collecting and analyzing inspection data provide a way to monitor and assess the safety condition of bridges. This thesis presents new methods for data-driven risk assessment of bridges subject to corrosion, scour, and other degradation mechanisms. The research focuses on utilizing collected inspection data to evaluate the bridges' structural conditions through novel modeling approaches for structural risk analysis, including bridge fragility assessments. In light of the contents described above, the thesis covers three main topics: the impact of corrosion on bridge structures' seismic performance, the impact of foundation scour on the structural performance of bridges/foundation piles, and the ability to simulate other degradation mechanisms on structures. Each main topic consists of two subtopics to further explore the details of the research findings, and a total of six subtopics are investigated in this thesis: 1. Exploration of failure modes of aging structural columns considering the impacts of measured corrosion 2. Methodology to update fragility assessment through Bayesian inference to reduce the computational cost for bridge risk assessment 3. Methodologies to assess structural reliability accounting for physical phenomena after scour events, including the impacts of soil stress history, scour hole dimensions, and layered soils effects 4. Investigation of the influence of measured non-uniform scour on bridge responses 5. Fragility assessment of bridges utilizing both scour and corrosion inspection data 6. Methodology to increase the numerical robustness and accuracy of analyzing frame elements with a softening material constitutive behavior These six areas provide an increased understanding of the performance of bridges subject to corrosion and scour and provide a robust framework to assess the safety condition of bridges based on collected inspection data. In determining the safety of bridges across a transportation network, the framework allows accurate identification of the most vulnerable bridges and supports decisions to reduce bridges' vulnerability across the network.