Optimization of waste tyre steel fiber and rubber added foam concretes using Taguchi method and artificial neural networks

Abstract

This research presents a study on the waste tyre steel (WTS) and rubber particles (RP) added foam concrete using Taguchi method and Artificial neural networks (ANN) by revealing its potential utilization in construction industry. The fresh and hardened characteristics of foam concretes were experimentally tested. Twenty-five different samples were first prepared according to the Taguchi design and compressive and flexural strengths, dry density and thermal conductivity of the samples were optimized. A Quasi-Newton based ANN algorithm was proposed to obtain prediction equations for the flexural and compressive strength of foam concretes. ANOVA analysis was also conducted to determine the most important factors. The results showed that the combined utilization of WTS and RP decreases the harmful effect of RP on the strength properties of foam concretes. Taguchi optimization results were confirmed through validation tests. With the proposed ANN method, R2 values were obtained as 0.9775 for compressive strength, 0.9723 for flexural strength, 0.9654 for dry density and 0.9527 for the thermal conductivity measurements. Both Taguchi and ANN system were found to be suitable for the design and estimation of the responses.