Open AccessBond strength prediction of the composite rebars in concrete using innovative bio‐inspired models
Author(s) -
Alizadeh Fatemeh,
Naderpour Hosein,
Mirrashid Masoomeh
Publication year - 2020
Publication title -
engineering reports
Language(s) - English
Resource type - Journals
ISSN - 2577-8196
DOI - 10.1002/eng2.12260
Subject(s) - structural engineering , reinforcement , fibre reinforced plastic , reinforced concrete , bond , fuzzy logic , corrosion , bond strength , composite number , beam (structure) , materials science , artificial neural network , solid mechanics , computer science , composite material , engineering , layer (electronics) , artificial intelligence , adhesive , finance , economics
The bond strength among the concrete and reinforced bars is one of the most important factors in designing the reinforced concrete structures. One of the serious problems encountered by utilization of steel‐reinforcement bars is corrosion when exposed to different environments. Glass fiber‐reinforced polymers (GFRPs) are known as a solution to prevent the destruction of the civil infrastructure. The present study attempts to predict the bond strength between the GFRP bars and concrete based on the neuro‐fuzzy inference system and artificial neural networks using 159 beam specimens including notched, splice, hinged, and inverted hinged. The neuro‐fuzzy inference system consists of five input series. The output, which is the bond strength, is compared with those presented in the various code relations such as ACI 440.1R‐15 and CSA S806‐12. It was concluded that the outputs are more compatible with the experimental results in comparison with the amounts obtained from the codes equations.