Artificial neural network to predict the compressive strength of high strength self-compacting concrete made of marble dust

ABSTRACT

The construction industry is continually seeking new waste materials and techniques to enhance the sustainability and overall performance of concrete. High Strength Self-Compacting Concrete (HSSCC) is a type of concrete suitable for modern construction that offers superior mechanical properties and excellent workability. In this investigation, the compressive strength of HSSCC containing varying proportions of marble dust is predicted using an Artificial Neural Network (ANN). An exhaustive dataset collected from laboratory tests encompasses a variety of mix designs with different proportions of marble dust. The integration of marble dust, a by-product of the marble industry, into HSSCC gives a sustainable approach for overall performance of concrete. The parameters considered in the studies include the water-cement ratio, marble dust content, and quartz sand content. The results indicate that the ANN model can accurately predict the compressive strength of HSSCC. The key finding indicate architecture 3-4-1 was found to be the most effective in achieving a high regression value of 0.937.

Keywords:
High Strength Self-Compacting Concrete; Artificial Neural Network; Marble dust; Mechanical properties