The prediction and evaluation of recycled polypropylene fiber and aggregate incorporated foam concrete using Artificial Neural Networks

Abstract

Reinforcement with recycled fiber is widely investigated to improve the mechanical behavior of foam concrete. In addition, the use of recycled aggregate in concrete provides benefits in terms of sustainability as it reduces the use of raw materials. In this research, 6 mm-long fibers and aggregates obtained from waste polypropylene were utilized in foam concrete production. This study also presents an evaluation of the recycled polypropylene fiber (PppF) and aggregate (PppA) incorporated foam concretes using ANOVA and ANNs. The ANN model was developed to estimate the compressive and flexural strengths, dry density, and thermal conductivity. The results indicated that the use of recycled polypropylene fiber increased the compressive and flexural strengths, however, polypropene aggregates affected the strengths negatively. And mixtures with higher levels of RppA and RppF have lower thermal conductivities. The slump of fresh concrete had an apparent reduction with the increase in RppA quantity. Also, both the ANN and ANOVA approaches were appropriate for optimizing and estimating responses.