Sharif Digital Repository

In this study, a two-layer feed-forward neural network was constructed and applied to determine a mapping associating mix design and testing factors of cement-nano silica (NS)-rice husk ash ternary blended concrete samples with their performance in conductance to the water absorption properties. To generate data for the neural network model (NNM), a total of 174 field cores from 58 different mixes at three ages were tested in the laboratory for each of percentage, velocity and coefficient of water absorption and mix volumetric properties. The significant factors (six items) that affect the permeability properties of ternary blended concrete were identified by experimental studies which were:... 

The aim of this study was to investigate the influences of combination of the two different SiO2 nanoparticles (15 nm and 80 nm) on compressive, flexural and tensile strength of ternary blended concrete. SiO2 nanoparticles with two different sizes of 15 and 80 nm have been used as a partial cement replacement by 0.5, 1.0, 1.5 and 2.0 wt.% in 16 different proportions of mixture followed by curing in lime solution for 7, 28 and 90 days. The results indicate that in all curing ages in lime solution specimens with 2.0% of 15 nm plus 1.5% of 80 nm cement replacement achieved higher mechanical properties. The continuous cement paste with the lowest delicate zones might be due to the fact of quick...