Bayesian-Based Assessment of Retrofitting Strategies for Seismic Resilience of Petroleum Storage Tanks
Keywords:
Bayesian causal inference, Seismic retrofitting, Petroleum storage tanks, Fragility manifolds, Structural resilienceAbstract
This study presents a causally informed, Bayesian-based framework for evaluating seismic retrofitting strategies applied to an uplift-sensitive petroleum storage tank. A nonlinear digital twin is integrated with hierarchical Bayesian inference and counterfactual analysis to quantify probabilistic fragility transformation and mechanism-specific retrofit effects. The investigated reference tank exhibits a coupled uplift–localization–instability cascade governing severe damage evolution. Shell confinement, anchorage enhancement, and hybrid configurations are evaluated within a unified causal architecture. Results demonstrate that single-mechanism interventions provide partial mitigation and exhibit saturation under high-demand regimes, whereas hybrid retrofits simultaneously suppress uplift persistence and shell instability sensitivity, producing reduced fragility curvature, compressed uncertainty, and enhanced resilience robustness. Bayesian posterior reconstruction reveals heterogeneous treatment effects across demand and operational states, invalidating deterministic retrofit ranking assumptions. The proposed framework enables multidimensional fragility manifold reconstruction and mechanism-resolved resilience assessment, providing a rigorous basis for uncertainty-aware retrofit selection in uplift-dominated petroleum storage systems.
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