shear strength prediction of rc beams using adaptive neuro-fuzzy inference system

In complex engineering problems, there are some inexact conceptions or a lot of parameters that must be considered. soft computing is an approach that is successfully applied to solve such problems. determination of fuzzy rules for many problems has not been quite possible by an expert human. in this case, a neuro-fuzzy system that is a combination of neural network (for its ability to learn by datasets) and fuzzy system (for solving the drawback of the neural network) can enhance the performance of the system with several parameters or complex conditions. this paper shows the capability of a neurofuzzy system, namely adaptive neuro-fuzzy inference system (anfis), to predict the shear strength of reinforced concrete (rc) beams with steel stirrups. for this purpose, the collection of laboratory results published by different works of literature was used to train and test the proposed system. for this purpose, the sub-clustering (sc) approach was applied to generate anfis. the results indicated that the considered neuro-fuzzy system was able to predict the shear strength of the rc beams, which have been reinforced with steel stirrups.