Presented By: Weina Meng, Stevens Institute of Technology
Description: High-performance fiber reinforced cementitious composite (HPFRCC) features high compressive and flexural strength, strain hardening behavior, and long-term durability due to dense microstructure. The design of novel HPFRCC mostly depends on intensive experiments for trial and error, which is costly and inefficient. In addition, HPFRCCs exhibit dense microcracks whose opening widths are controlled within 100 µm. With narrow cracks, the cracked HPFRCCs have lower permeability and higher self-healing capability than conventional concrete. Thus, it is important to investigate the development process and the pattern of cracks in HPFRCCs, in order to understand the damages and degradation concerning the safety and durability. Conventional visual inspection and manual measurement for dense and microcracks is time-consuming and labor intensive. To accelerate the mixture development and characterization of HPFRCC, some advanced machine learning approaches have been developed: (1) Web clawer and table extraction methods were applied to extract mixture design information used to train machine learning models. (2) Machine learning methods were trained to predict key properties of HPFRCC. (3) Generative adversarial networks are used to generate cracked HPFRCC to enlarge the dataset. (4) Deep learning based semantic segmentation models are trained to convert the RGB image into binary image for crack identification and quantification.