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EIGAN is a novel deep-learning framework known as an unpaired image-to-image translation model, designed to simulate damage on infrastructure imagery while preserving structural geometry—bridges, buildings, roads, etc. Based on Cycle-Consistent Adversarial Networks, it learns to convert undamaged images into realistic “damaged” versions without needing direct before/after pairs for training. EIGAN excels in creating synthetic datasets by automatically generating diverse damage scenarios that closely resemble real-world wear and faults after events like earthquakes. These enhanced datasets help train more robust AI models for infrastructure health monitoring, improving accuracy and enabling safer, more automated structural assessments (sail.cive.uh.edu).