ANN- PSO modelling for predicting buckling of self-compacting concrete column containing RHA properties

ABSTRACT

In recent decades, concrete technology has reached the broad-based areas of operations through the implementation of Self Compacting Concrete to increase the concrete performance. Due to high silica content, the pozzolonic characteristic of RHA makes it as a supplementary material for cement. In this paper, Cement was partially replaced with Rice Hush Ash of 5%, 10% 15%, 20%, 30% and 40% influencing the properties of SCC. The aim of this report is to explore the effect of cement replacement by RHA on the fresh and mechanical properties of SCC. In addition, the bucking behaviour of the axial loaded reinforced concrete column was predicted using Artificial Neural Network. Experimental data are collected and 100 experimental data is used for training the ANN model and 20 sets of data is utilized for testing. From the results it is observed that SCC blended with RHA shows the positive relationship between 30% replacement of RHA with an increase in the strength of compression and tensile strength of around 6. The buckling behaviour of the 70% Cement + 30% RHA SCC reinforced column was predicted by ANN- PSO is a precision and efficient model.

Keywords
Self-compacting concrete; Rice Husk Ash; Artificial Neural Network; Fresh properties; Mechanical properties; Buckling of Column