Due to the prohibitive cost of transporting raw materials into Space, in-situ materials along with cement-like binders are poised to be employed for extraterrestrial construction. A unique methodology for obtaining microstructural topology of cement samples hydrated in microgravity environment at the International Space Station (ISS) is presented here. Distinctive Scanning Electron Microscopy (SEM) micrographs of hardened tri-calcium silicate (C₃S) samples were used as exemplars in a deep learning-based microstructure reconstruction framework. The proposed method aids in generation of an ensemble of microstructures that is inherently statistical in nature, by utilizing sparse experimental data such as the C₃S samples hydrated in microgravity. The hydrated space-returned samples had exhibited higher porosity content (~70 %) with the portlandite phase assuming an elongated plate-like morphology. Qualitative assessment of the volumetric slices from the reconstructed volumes showcased similar visual characteristics to that of the target 2D exemplar. Detailed assessment of the reconstructed volumes was carried out using statistical descriptors, and was further compared against micro-CT virtual data. The reconstructed volumes captured the unique microstructural morphology of the hardened C₃S samples of both space-returned and ground-based samples, and can be directly employed as Representative Volume Element (RVE) to characterize mechanical/transport properties.

由于将原材料运输到太空的成本高昂，现场材料和水泥状粘合剂准备用于外星建筑。本文介绍了一种独特的方法，用于获取国际空间站 (ISS) 微重力环境下水合水泥样品的微观结构拓扑。硬化硅酸三钙 (C ₃ S) 样品的独特扫描电子显微镜 (SEM) 显微照片被用作基于深度学习的微观结构重建框架的范例。_{所提出的方法通过利用稀疏实验数据（例如在微重力下水合的 C 3} S 样品），有助于生成本质上具有统计性​​质的微观结构集合。水合太空返回样品表现出较高的孔隙率含量（约 70%），硅钙石相呈现细长的板状形态。对重建体积的体积切片的定性评估显示出与目标二维样本相似的视觉特征。使用统计描述符对重建体积进行详细评估，并与微 CT 虚拟数据进行进一步比较。重建的体积捕获了太空返回和地面样品的硬化 C _{3 S 样品的独特微观结构形态，并且可以直接用作代表性体积单元 (RVE) 来表征机械/传输特性。}