This study proposes a self-aware retrofitting framework for ground-supported steel petroleum storage tanks that integrates embedded structural sensing with Bayesian causal damage localization to predict and mitigate seismic buckling risk. Unlike conventional monitoring that reports damage after occurrence, the proposed system extracts causal indicators—energy localization, uplift activity, and shell softening gradients—from strain, acceleration, and uplift measurements and uses Bayesian inference to identify future-critical zones before global instability. A controlled low-variance synthetic benchmark is adopted to ensure numerical stability and isolate mechanism-driven effects across intensity-scaled ground motions. Results show that the self-aware intervention reduces mean buckling probability from 0.63 (baseline) to 0.17 and collapses normalized global response variance by approximately 22%, while enabling targeted strengthening with substantially lower material demand compared to uniform retrofitting. The findings demonstrate that adaptive structural intelligence can transform storage tanks from passive components into predictive, damage-aware infrastructure assets, providing a replicable pathway for resilient energy-system continuity under seismic uncertainty.