The microstructure of cement paste determines the overall performance of concrete and therefore obtaining the microstructure is an essential step in concrete studies. Traditional methods to obtain the microstructure, such as scanning electron microscopy (SEM) and X-ray computed tomography (XCT), are time-consuming and expensive. Herein we propose using Denoising Diffusion Probabilistic Models (DDPM) to synthesize realistic microstructures of cement paste. A DDPM with a U-Net architecture is employed to generate high-fidelity microstructure images that closely resemble those derived from SEM. The synthesized images are subjected to comprehensive image analysis, phase segmentation, and micromechanical analysis to validate their accuracy. Findings demonstrate that DDPM-generated microstructures not only visually match the original microstructures but also exhibit similar greyscale statistics, phase assemblage, phase connectivity, and micromechanical properties. This approach offers a cost-effective and efficient alternative for generating microstructure data, facilitating advanced multiscale computational studies of cement paste properties.

Portland cement paste has a highly heterogenous evolving microstructure that complicates the development of stronger and greener cementitious materials. Microstructure is the fundamental input of multiscale studies on material behaviors. Herein, we propose a conditional generative AI framework for synthesizing high-fidelity 3D microstructures of hydrating cement paste (1–28 days) with varying water-to-cement ratios and Blaine fineness values. A latent diffusion transformer, operating within a compact two-stage latent space derived via a vector quantized variational autoencoder, efficiently captures and reproduces experimentally measured microstructural patterns. Statistical analyses confirm strong consistency in grey value distributions, micromechanical properties, hydration phase evolution, and particle size distributions, with only minor boundary-related discrepancies. Validation using a pretrained classifier further corroborates the fidelity of generated microstructures. This approach provides a robust tool for realistic cement paste microstructure generation, supporting multiscale modeling and advancing the design of sustainable cementitious materials.

The permeability of natural aggregate is close to cement mortar with relative lower w/c ratios, therefore when calculating the overall concrete water permeability, the effect of aggregate permeability cannot be neglected. This paper presents a sophisticated 3D three phase meso-scale model based on an efficient method of generating random ellipsoidal particles within confined cylindrical space. The meso-scale model considers concrete as the combination of mortar, aggregate and interfacial transition zone, and is used to characterize the permeability of concrete. Furthermore, a series of permeation experiments of concrete with different w/c ratios and aggregate volume fractions are conducted to exemplify the effects of aggregates on concrete water permeability and provide parameters and verification for numerical models. The effects of aggregate permeability on concrete water permeability is evaluated based on both experimental and numerical results. And the permeability coefficient of aggregate adopted in the experiment is estimated reasonably and incorporated into further numerical predictions of concrete water permeability. By comparing the experimental and numerical results, the applicability of meso-scale model proposed here is validated and the effects of aggregate on water permeability of concrete with different w/c ratios vary from each other, depending on the ratio of water permeability of aggregate and mortar.