International Journal of Advance Civil Engineering and Technology ISSN: 3107-7374 (Online)

Authors:- Sabyath Shetty, Sameer kumar

Abstract:- This paper presents an investigation on innovative optimum, Self-compacting concrete (SCC) can produce much higher fluidity with no occurrence of segregation, thanks to its lower yield value and higher viscosity than conventional concrete. Increased productivity and improved working environment have had high priority in the development of concrete construction over the last decade. The casting process creates a significant amount of waste foundry sand (WFS). Using WFS as a concrete ingredient reduces the problems associated with the dumping process of these types of wastes, removes/reduces carbon dioxide, and is also considered economical in terms of overall concrete production cost. . Hence, predicting the behavior of concrete using the development of models based on artificial intelligence (AI) algorithms derived from the laboratory data can remarkably improve the project’s efficiency in terms of cost and time. This paper assessed the performance of artificial neural networks (ANNs) to predict the strength parameters of concrete containing WFS (CCWFS). In this regard, a comprehensive laboratory database consisting of 102, 397, 146, 346, and 169 data for the slump, compressive strength, elasticity modulus, splitting tensile strength, and flexural strength of CCWFS were collected from literature, respectively.

Keywords:- Waste foundry sand, Aritifical Neural Networks (ANNs), Multiple linear regression (MLR).