Understanding the mechanism of crack growth in cement-based materials under mechanical loading involves complex interactions between microstructural components, including aggregates, voids, and cement paste. This paper presents a unique approach that combines X-ray computed tomography (XCT) with deep learning to…
Understanding the mechanism of crack growth in cement-based materials under mechanical loading involves complex interactions between microstructural components, including aggregates, voids, and cement paste. This paper presents a unique approach that combines X-ray computed tomography (XCT) with deep learning to segment these components precisely. By leveraging XCT's high-resolution 3D imaging capabilities and the robustness of deep learning algorithms, our method provides a detailed characterization of the microstructure of cement-based materials. This detailed structural information is crucial for understanding crack initiation and propagation processes, ultimately contributing to developing more durable and sustainable concrete. Our results highlight the significant potential of deep learning in enhancing our understanding of damage and failure mechanisms in cement-based materials, providing valuable insights that can lead to improved material performance and longevity.