Prediction of the Mechanical Properties of Lightweight Basalt Fiber Reinforced Concrete Containing Silica Fume and Fly Ash with an Adaptive Neuro-fuzzy inference system (AFNIS)

Most studies show that some certain fiber can enhance to some extent both mechanical properties as well as ductility of lightweight concrete. On the other hand, due to high industrial potential as well as less damage to the environment, basalt fibers have gradually become an appropriate option to be used in the composite industries and thus is considered as a special competitor to other fibers (Ralegaonkar et al., 2018; Sadrmomtazi et al., 2018). In recent years, there has been a growing interest in the use of the intelligent inference and reasoning methods, including ANFIS and ANN to approximate nonlinear and complex concrete behaviors and predict its properties (Tesfamariam and Najjaran, 2007; Sadrmomtazi et al., 2013). Therefore, this study aimed to investigate mechanical properties of the lightweight structural concrete containing different amounts of basalt fibers (0, 0.1, 0.2, 0.3, 0.4, and 0.5%) as well as pozzolanic materials (silica fume and fly ash). In addition, the modified ANFIS was used as a robust method to predict results to evaluate the experimental results.