Estimating the Bond Strength of FRP Bars Using a Hybrid Machine Learning Model

Abstract

Although the use of fiber-reinforced plastic (FRP) rebars instead of mild steel can effectively avoid rebar corrosion, the bonding performance gets weakened. To accurately estimate the bond strength of FRP bars, this paper proposes a particle swarm optimization-based extreme learning machine model based on 222 samples. The model used six variables including the bar position (P), bar surface condition (SC), bar diameter (D), concrete compressive strength (fc), the ratio of the bar depth to the bar diameter (L/D), and the ratio of the concrete protective layer thickness to the bar diameter (C/D) as input features, and the relative importance of the input parameters was quantified using a sensitivity analysis. The results showed that the proposed model can effectively and accurately estimate the bond strength of the FRP bar with R2 = 0.945 compared with the R2 = 0.926 of the original ELM model, which shows that the model can be used as an auxiliary tool for the bond performance analysis of FRP bars. The results of the sensitivity analysis indicate that the parameter L/D is of the greatest importance to the output bond strength.