A Validation Study on Mechanical Properties of Foam Concrete with Coarse Aggregate Using ANN Model

Abstract

The usage of foam concrete (FC) was extended from being used as a filler material to an alternative concrete due to the effect of conventional concrete on global warming. The diversified perspective on FC as an alternative to conventional concrete is due to its low density (400–1800 kg/m3) and good thermal conductivity, which also results in the reduction of costs in production, labor, and transportation. Generally, FC is produced by adding a pre-made foam to the cement slurry consisting of cement and aggregates. Here, the study was carried out by the addition of a coarse aggregate and foaming agent (i.e., 12%, 6%, 3%, 2%, 1%) at varying percentages in FC to improve the strength characteristics. FC was tested for its physical and mechanical properties. From the experimental results, an Artificial Neural Network (ANN) was developed to predict the strength of FC. The results from training and testing of the Polynomial Regression Analysis model (PRA) through ANN have shown great potential in predicting compression, split tensile, and flexural strength of FC. It was found that the strength of FC is increased with the reduction of foam volume and increase in coarse aggregate volume. However, a strength of 25.6 N/mm2 is achieved when 1% foam and 50% coarse aggregate is used.

Keywords

• foam concrete
• Artificial Neural Network
• polynomial regression analysis
• mechanical properties
• density