Parameterized Fragility Assessment of Coastal Structures: Capturing the Influence of Neighboring Structures

Abstract Structures and infrastructure are often densely distributed in coastal regions and highly susceptible to hurricane-induced stressors. Evidence from past hurricane events and experimental studies demonstrates that neighboring structures significantly alter the structure-to-structure demand induced by hurricane-related stressors, such as storm surge and wave loads, thereby influencing the damage pattern observed across a region. Capturing this modified load requires detailed computational fluid dynamics (CFD) analysis, making it difficult to incorporate such effects into current regional risk assessments. This paper proposes a framework for incorporating the modified loads directly into the formulation of parameterized fragility functions in a practical way. Our methodology uses a set of parameters to describe the portfolio layout, and its associated effects on the load modifications (i.e., shielding or channeling) are captured from detailed CFD analyses. The influence of the modified loads on the structural failure probabilities is embedded within the fragility model by considering the portfolio layout parameters during the model fitting procedure. To reduce the high computational expense of CFD analysis, active learning algorithms are leveraged to selectively choose only the most critical parameter combinations for numerical analysis. The proposed framework is demonstrated for aboveground storage tanks (ASTs) located within a tank farm, as a representative structural portfolio. The derived parameterized fragility models reveal that failure probability estimates vary significantly depending on the spatial arrangement of structures within the portfolio. Subsequent scenario-based risk estimates leveraging these fragilities show differences of 21.5% to 54.5% in failure probability and 31.4% in expected volume of hazardous material spills, compared to estimates obtained without considering the influence of neighboring structures. The proposed framework offers a path to improve risk assessment of densely arranged portfolios. We expect our contribution to render tailored structural interventions by revealing heterogeneous impacts, thus mitigating (or reducing) investments when dealing with coastal structural portfolios.