Applications of machine learning methods for design and characterization of high-performance fiber-reinforced cementitious composite (HPFRCC): a review

High-Performance Fiber-Reinforced Cementitious Composite (HPFRCC) represents a family of advanced composite materials with remarkable mechanical properties and durability, but their design and characterization tasks involve unique challenges. Recently, advancements in machine learning techniques have offered new opportunities. This paper reviews the application of machine learning techniques in the design and characterization of HPFRCC. The application of machine learning to the design of HPFRCC is reviewed based on a prediction-optimization framework, and the steps for property prediction and material design considering fresh properties, cracks, and microstructures are elaborated. The latest development of knowledge-guided machine learning approach is discussed. The application of machine learning to the characterization of HPFRCC is reviewed, and the computer vision and deep learning techniques for characterizing HPFRCC are elaborated. The challenges and opportunities for the applications of machine learning methods are discussed, aiming to facilitate applications of machine learning techniques for HPFRCC.