Flexural strength estimation of basalt fiber reinforced fly-ash added gypsum based composites

Abstract

Gypsum and fiber reinforced gypsum-based composites are widely used in many construction projects. Many researches have been conducted on these types of composites; however, there are very limited literature studies focused on the estimation of flexural strength of the fiber reinforced gypsum-based composites. And there is no current study on the mechanical strength estimation of these types of composites. An extensive experimental data covering 275 flexural strength test results with five input parameters (fly-ash, basalt, superplasticizer contents, water to binder ratio and slump values) were utilized for the artificial neural network (ANN) model designation. Effect of each parameters on the flexural strength were carefully analyzed. An empirical formula was developed with the generalization capabilities of the proposed ANN system, as well. Analysis results showed a good agreement between the site and field data. And these highly efficient numerical data can make it possible to predict flexural strength of gypsum-based composites.