Reconstruction of 3D Concrete Microstructures Combining High-Resolution Characterization and Convolutional Neural Network for Image Segmentation
After water, concrete is the second most used material in the world. Concrete’s forming adaptability and low-cost constituents make it a predominant material used in the construction of civil infrastructures in nuclear power plants such as concrete biological shields, containment buildings, turbine buildings, fuel handling and storage buildings, underground piping for cooling, cooling towers, and so on. Depending on environmental and operating conditions, these passive structures are subject to time-dependent phenomena that can either enhance (e.g., continued hydration) or degrade concrete’s structural performance. Unlike components such as the reactor pressure vessel and the primary circuit, concrete composition varies regionally because it is manufactured using local aggregates and cement. Hence, concrete performance metrics over time cannot be derived confidently using empirical relations. Alternatively, the specific characteristics of the local concrete constituents and their assemblage in concrete must be considered.