The objective of this study was to examine the shrinkage and creep of reactive power concrete (RPC) with different steel fibre contents (0%, 1% and 2% by volume). A total of 37 RPC specimens were prepared and tested for compression strength, elastic modulus, shrinkage, and creep. In addition, different axial stress ratios (0.2, 0.3 and 0.4) were used in the creep tests. Furthermore, the accuracy of the ACI 209-82 model, CEB-FIP 90 model, B3 model, and GL 2000 model for predicting the shrinkage and creep of RPC was evaluated and new numerical shrinkage and creep models were developed. The experimental results revealed that the compressive strength and elastic modulus increase with increasing steel fibre content. The shrinkage and creep decreased with increasing addition of steel fibre from 0% to 2%. A good linear relationship was found between the axial stress ratios and creep strain. All four existing models were unable to accurately predict the shrinkage and creep of RPC. A good agreement between the experimental results and proposed shrinkage and creep numerical models was observed. Therefore, it is suggested that the proposed shrinkage and creep models can be used to calculate the shrinkage and creep of RPC.

The prediction and control of the mechanical behaviours of fibre-reinforced polymer (FRP)-confined circular concrete columns subjected to axial loading are directly related to the safety of the structures. One challenge in building a mechanical model is understanding the complex relationship between the main parameters affecting the phenomenon. Artificial intelligence (AI) algorithms can overcome this challenge. In this study, 298 test data points were considered for FRP-confined circular concrete columns. Six parameters, such as the diameter-to-fibre thickness ratio (D/t) and the tensile strength of the FRP (f_frp) were set as the input sets. The existing models were compared with the test data. In addition, artificial neural networks (ANNs) and support vector regression (SVR) were used to predict the mechanical behaviour of FRP-confined circular concrete columns. The study showed that the predictive accuracy of the compressive strength in the existing models was higher than the peak compressive strain for the high dispersion of material deformation. The predictive accuracy of the ANN and SVR was higher than that of the existing models. The ANN and SVR can predict the compressive strength and peak compressive strain of FRP-confined circular concrete columns and can be used to predict the mechanical behaviour of FRP-confined circular concrete columns.

Alkali-activated materials have attracted increasing interest owing to their excellent properties and environmental protection. However, there have been few studies on their creep properties. The aim of this article is to investigate the effect of the stress–strength ratio on the creep property of sodium silicate–based alkali-activated slag (AAS) concrete. For this reason, five groups of AAS concrete with different stress–strength ratios (0.15, 0.3, 0.45, 0.6, and 0.75) were tested. The results indicate that the creep of AAS concrete has a convergent nonlinear stage and a non-convergent stage but not an obvious linear stage. The AAS concrete basically has a consistent creep coefficient and diverse specific creep under a stress–strength ratio of 0.15–0.6. The elasticity modulus of AAS is much smaller than that of ordinary Portland cement (OPC) concrete, which is the reason for the greater creep compared to that in OPC concrete, and the inaccuracy of the model prediction. By applying the actual elastic modulus, the models can predict the specific creep and stress-dependent strain of AAS concrete with a 0.3 stress–strength ratio, except for the B3 model. The secant modulus of AAS concrete decreases linearly with an increase in the stress–strength ratio. Finally, we propose an improved creep model for AAS concrete with a wide stress–strength ratio based on the GL2000 model.