Effects of volume fraction and length of carbon short fibers on flexural properties of carbon textile reinforced engineered cementitious composites (ECCs); an experimental and computational study. (10th June 2020) - Halvaei, Mana; Jamshidi, Masoud; Latifi, Masoud; Ejtemaei, Mojtaba
Highlights: Carbon fabric used as reinforcement of engineered cementitious concrete (ECC) not PVA short fibers. Carbon chopped fiber (CCF) used as hybrid with carbon fabric (CT) in ECC formulation. Effect of volume fraction and length of CCF on the properties CT reinforced ECC was investigated. The effects of textile characteristics (i.e. weaving density and mesh size) on properties were studied. Abstract: Cement based materials are brittle under tensile and flexural loadings. Nowadays, the advantages of utilizing short fibers and textiles for reinforcing cementitious composites have been well known. In this research, simultaneous usage of carbon textiles and chopped carbon fibers as reinforcement of engineered cementitious composite (ECC) was investigated. This was performed to obtain advantages of both reinforcements such as increasing in the first crack strength and strain hardening behavior. The four-point bending test was carried out to evaluate flexural behavior of the samples. The effects of textile mesh size and chopped fibers length and content on the flexural properties of the samples were evaluated. It was found that the first crack and post crack loads as well as the flexural toughness significantly increased with an increase in the fibers volume content. The first crack load of the textile reinforced samples were improved 25–240% by utilizing the short fibers. The flexural toughness of the samples contained hybrid of short fibers and textiles were also increased 7–57% compared to the samples reinforced just by carbon textiles. In the next step, artificial neural network (ANN) was applied in order to predict the flexural strength of the concrete samples reinforced by hybrid of the carbon textiles and carbon fibers. It was found that the predicted data are in a good correlation with the experimental data.