Yonsei University · Engineering
Professor Tong-Seok Han's research lab specializes in computational materials science and micromechanics, focusing on the 3D microstructure characterization and mechanical property prediction of cement-based materials. The lab integrates advanced imaging techniques such as micro-CT with finite element analysis and artificial intelligence, particularly generative adversarial networks (GANs), to reconstruct and simulate multi-phase cement paste microstructures. Key research directions include phase connectivity analysis, anisotropic microstructural modeling, and the development of data-driven frameworks for accelerating materials design and performance evaluation. The lab aims to bridge the gap between microstructure evolution and macroscopic mechanical behavior in construction materials.
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The behavior of cement paste is significantly affected by the phase distribution characteristics within the material. However, studying the correlation between the microstructural characteristics and intrinsic properties involves time-consuming experiments. Thus, simulations can help accelerating the process. In this study, a method using micro-CT imaging analysis and nanoindentation within the framework of finite element analysis was proposed to investigate the mechanical properties of the ceme
A quantitative measure of anisotropic phase connectivity is investigated. The isotropic stereological parameter, contiguity, is extended to characterize the anisotropic phase connectivity. Two mathematical representations for characterizing the anisotropic contiguity are presented, one based on C0 piecewise polynomials and the other on tensorial expansions. The anisotropic contiguities for several virtual specimens having different phase morphologies are investigated with the aid of a numerical
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 ex
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