Estimation of the bond strength between FRP and concrete using ANFIS and hybridized ANFIS machine learning models

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Thuy-Anh Nguyen
Hai-Bang Ly


Adaptive Neuro-Based Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) algorithms were utilized to produce numerical tools for predicting the bond strength between the concrete surface and carbon fiber reinforced polymer (CFRP) sheets. From the relevant literature, a credible database encompassing 242 test specimens was developed, along with six input factors that primarily determine bond strength. These characteristics include the beam's width, the compressive strength of the concrete, the FRP thickness, the FRP modulus of elasticity, the FRP length, and the FRP width. Finally, using conventional statistical metrics, the outputs of the two suggested models (ANFIS and ANFIS-PSO) were compared to the experimental data. Both models were shown to be a good alternative strategy for predicting the bond strength of FRP-to-concrete.

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