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Reconstruction of lost responses under external loads, e.g. ambient and seismic loading conditions, is important for structural health monitoring to evaluate the safety of structures. This paper proposes a Segment based Conditional Generative Adversarial Network (SegGAN), which is a powerful deep learning model for solving pixel-to-pixel tasks, to conduct structural dynamic response reconstruction. The proposed network consists of a bottlenecked generator and a segment based discriminator. Generator features skip and dense connections to improve feature extraction, and segment based discriminator uses conditioned input to facilitate generator to learn detailed and robust features. Numerical studies on a steel frame structure model are conducted to evaluate the accuracy and noise immunity of using SegGAN for structural response reconstruction. The feasibility of using the reconstructed response for damage …