Thermal performance evaluation of fiber reinforced concrete treated with eco friendly additives

Abstract

A concrete fiber was necessary to improve the properties of engineering. Various kinds of fibers are used as a reinforcing component. The use of natural concrete fiber was widely increased because of the eco-friendly nature, low expense and availability. Compared to other fibers, jute fiber was very cheap and accessible in tropical countries. This research evaluates various approaches related to soft computing. Some of them are Fuzzy Neural Networks, Support Vector Regression for the growth of nonlinear designs which judge the mechanical property like tensile and compressive strength of JFRCC and response surface methodology. These things are dependent mainly on the proportion of water and cement content, volume and length of jute fiber. The codes for support vector regression and fuzzy neural networks are written in Matrix Laboratory (R-2018), while Minitab 19 software statistics was used for producing experimental model matrix and box behnken model. Information for the things of jute fiber reinforced concrete composite are existed depend upon this model matrix and these information are developed to utilize, evaluate and compare the recommended designs. The output denote that support vector regression design performs very well than response surface methodology and fuzzy neural networks designs with the analysis of different activities calculating parameters such as error in root mean squared, relative, mean absolute, residual, correlation coefficient and fractional bias for analyzing the tensile strengths and compressive.