Analysis of compressive strength of sustainable fibre reinforced foamed concrete using machine learning techniques

Abstract

This study emphasizes the usage of Silica Fume (SF) and Marble Sludge Powder (MSP) as a partial replacement for fly ash in Fibre Reinforced Foamed Concrete (FRFC). The compressive strength for various samples was analyzed using Artificial Neural Network (ANN) methods. In this research work, the utilization of silica fume, fly ash, marble sludge powder, polypropylene fiber, and foaming agent in fiber-reinforced foamed concrete is presented and a sincere attempt has been made to use silica fume and marble sludge powder for the replacement of fly ash with various percentages. In addition to that polypropylene fiber (PPF) was used in various proportions of 0%, 0.1%, 0.2%, 0.3%, 0.4%, and 0.5%. The Feed Forward Propagation (FFP) network of the machine learning method with one hidden layer was taken as the ANN structure of FRFC. In this ANN work, cement, silica fume, fly ash, marble sludge powder, foaming agent, water, and polypropylene fiber were used as input parameters and compressive strength is the output parameter. The correlation coefficient with the ANN methods was found as 0.940 for compressive strength. In machine learning techniques, the ANN method was found to be accurate in estimating and analyzing strength prediction responses with effective parameters.

Keywords

• fibre reinforced foamed concrete
• polypropylene fiber
• artificial neural network
• compressive strength
• feed forward propagation