Prediction of shear debonding strength of concrete structure with high-performance fiber reinforced concrete

Abstract

Debonding of the fiber-reinforced concrete reinforcement is counted as significant matter in concrete design because of the shear stresses. The main issue is the potential of brittle debonding failures that could highly reduce the effectiveness of strengthening application. Shear bond strength and the governing variables have been empirically analyzed several times; however, these experiments couldn't provide accurate predictions because of the complexity of debonding process. In this study was analyzed debonding behavior of concrete structure with high-performance fiber reinforced concrete by adaptive neuro fuzzy inference system (ANFIS). High-performance fiber reinforced concrete could be used as repairing material for normal concrete structures. In this study the concrete structure with high-performance fiber reinforced concrete was subjected to shear loadings and corresponding data samples has been acquired for ANFIS analyzing. Mechanical surface treatment with and without chemical substitute was used as bonding strategies for fabrication of samples. Finite element method is used for data samples extraction. ANFIS methodology was used for data samples analyzing based on prediction accuracy of shear debonding strength. Influence of parameters on the shear debonding strength were investigated by ANFIS approach. Obtained results could be used for further improvement of the high-performance concrete structure.