A. M. N. El-Khoja and A. F. Ashour and J. Abdalhmid and X. Dai and A. Khan
Prediction of Rubberised Concrete Strength by Using Artificial Neural Networks
1068 - 1073
2018
12
11
International Journal of Structural and Construction Engineering
https://publications.waset.org/pdf/10009743
https://publications.waset.org/vol/143
World Academy of Science, Engineering and Technology
In recent years, waste tyre problem is considered as one of the most crucial environmental pollution problems facing the world. Thus, reusing waste rubber crumb from recycled tyres to develop highly damping concrete is technically feasible and a viable alternative to landfill or incineration. The utilization of waste rubber in concrete generally enhances the ductility, toughness, thermal insulation, and impact resistance. However, the mechanical properties decrease with the amount of rubber used in concrete. The aim of this paper is to develop artificial neural network (ANN) models to predict the compressive strength of rubberised concrete (RuC). A trained and tested ANN was developed using a comprehensive database collected from different sources in the literature. The ANN model developed used 5 input parameters that include coarse aggregate (CA), fine aggregate (FA), wc ratio, fine rubber (Fr), and coarse rubber (Cr), whereas the ANN outputs were the corresponding compressive strengths. A parametric study was also conducted to study the trend of various RuC constituents on the compressive strength of RuC.
Open Science Index 143, 2018