Predicting Bond Strength of Steel Reinforcement in Self-Compacting Concrete (SCC) Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

The advantages of using self-compacting concrete (SCC) are reducing the time of construction and the number of employments, reducing noise that can disturb the surrounding environment, and increasing the density of hardened concrete structural elements, automatically affecting bond strength reinforcement in SCC. The bond strength is a parameter as an essential factor affecting the behavior of reinforced concrete. In this manuscript, the Adaptive Neuro-Fuzzy Inference System (ANFIS) model was built to predict the bond strength in SCC. For showing the performance of the ANFIS model, the level of accuracy-based correlation coefficient (R2) and Root Mean Square Error (RMSE) were determined. Learning process data consists of input and output. The input in this study includes compressive strength of concrete (f'c), the diameter of steel reinforcement (db), and development of length (Ld), while the output bond strength ( ). The results of the proposed model were in good agreement with the experimental results, as evidenced by an R2 of 0.71 and an RMSE of 3.31 MPa in the testing data, indicating that the proposed ANFIS model is capable of accurately predicting steel reinforcement bond strength in SCC.