Sharif University of TechnologyScientia Iranica1026-309816120090201Predicting Density and Compressive Strength of Concrete Cement Paste Containing Silica Fume Using Articial Neural Networks3173ENH. KetabchiDepartment of Civil Engineering,Sharif University of TechnologyM.H. AfsharDepartment of Civil Engineering,Iran University of Science and TechnologyE. RasaDepartment of Civil and Environmental Engineering,University of CaliforniaJournal Article20090602Abstract. Articial Neural Networks (ANNs) have recently been introduced as an ecient articial
intelligence modeling technique for applications involving a large number of variables, especially with
highly nonlinear and complex interactions among input/output variables in a system without any prior
knowledge about the nature of these interactions. Various types of ANN models are developed and used
for dierent problems. In this paper, an articial neural network of the feed-forward back-propagation
type has been applied for the prediction of density and compressive strength properties of the cement paste
portion of concrete mixtures. The mechanical properties of concrete are highly in
uenced by the density
and compressive strength of concrete cement paste. Due to the complex non-linear eect of silica fume on
concrete cement paste, the ANN model is used to predict density and compressive strength parameters. The
density and compressive strength of concrete cement paste are aected by several parameters, viz, watercementitious
materials ratio, silica fume unit contents, percentage of super-plasticizer, curing, cement
type, etc. The 28-day compressive strength and Saturated Surface Dry (SSD) density values are considered
as the aim of the prediction. A total of 600 specimens were selected. The system was trained and validated
using 350 training pairs chosen randomly from the data set and tested using the remaining 250 pairs.
Results indicate that the density and compressive strength of concrete cement paste can be predicted much
more accurately using the ANN method compared to existing conventional methods, such as traditional
regression analysis, statistical methods, etc.https://scientiairanica.sharif.edu/article_3173_454f1a607cb905002607a3539b12f4a3.pdf