To improve the weak mechanical properties of the foamed concrete that resulted from a large number of pores, a plant fiber is used as fill with the matrix. In this study, five contents of sisal fiber are added into the foamed concrete to investigate the static and fatigue performance of composites. The static and fatigue experimental results show that the sisal fiber can improve the mechanical properties of the foamed concrete. When the content of sisal fiber is less than 0.15%, the higher the content of sisal fiber, the greater the bending strength and fatigue life of the foamed concrete. In contrast, if the sisal fiber content is larger than 0.15%, the bending strength and fatigue life decrease with the increasing fiber content. From the regression analyses, the results show that the double linear relationships exist not only between the sisal fiber content and the flexural strength, but also between the sisal fiber content and the fatigue life of the foamed concrete. In this study, the optimal content of sisal fiber mixed in the foamed concrete can be inferred to be 0.133%.

The durability of reinforced concrete (RC) beams strengthened with carbon fiber-reinforced polymer (CFRP) is a worldwide concern in structural engineering. As an important part of the strengthened beam, the performance of the CFRP–concrete interface under hygrothermal environments is a delicate problem. In this paper, the fatigue behavior of CFRP-strengthened RC beams is analyzed by a theoretical model. In the model, CFRP–concrete interface degradation under hygrothermal environments is involved. Since interface debonding and rebar fracture induced by intermediate cracking are two typical failure modes, the damage models of rebar and the CFRP–concrete interface are established. Based on the theoretical model, the failure mode of CFRP-strengthened RC beams can be predicted, and fatigue life can be determined. The results showed that IC debonding is more likely to occur under hygrothermal environments. The accurate prediction of failure modes is essential for fatigue life prediction.