This study proposes an artificial intelligence based framework for reconstructing the 3D multi-phase cement paste microstructure to evaluate its mechanical properties using simulation. The reconstruction of cement paste microstructures is performed using modified generative adversarial networks (GANs) based on microstructural images from micro-CT. For computational efficiency, 2D microstructures are first reconstructed and then extended to 3D microstructures. The reconstructed microstructures exhibit the same microstructural features as the original microstructures when characterized by probability functions. Mechanical properties such as stiffness and tensile strength are evaluated for the original and reconstructed specimens using a phase-field fracture model, and similar behaviors are observed. The results confirm that the reconstructed virtual microstructures can be used to supplement the real microstructures in evaluating the mechanical properties of 3D multi-phase cement paste. This approach thus provides a critical element of a data-driven approach to correlating its microstructure and properties.

中文翻译：

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使用生成对抗网络重建 3D 多相水泥浆体微结构的力学性能评估


本研究提出了一种基于人工智能的框架，用于重建 3D 多相水泥浆体微观结构，以通过模拟评估其机械性能。水泥浆微观结构的重建是使用基于微型 CT 微观结构图像的改进生成对抗网络 (GAN) 进行的。为了提高计算效率，首先重建 2D 微观结构，然后扩展到 3D 微观结构。当用概率函数表征时，重建的微观结构表现出与原始微观结构相同的微观结构特征。使用相场断裂模型评估原始和重建样本的机械性能，例如刚度和拉伸强度，并观察到类似的行为。结果证实，重建的虚拟微观结构可以用来补充真实微观结构，以评估 3D 多相水泥浆体的力学性能。因此，这种方法提供了数据驱动方法的关键要素，以关联其微观结构和特性。