Experimental study on flexural behaviour of reinforced concrete beams strengthened with passive and active CFRP strips using a novel anchorage system

The paper presents the research on reinforced concrete (RC) beams strengthened with carbon fibre reinforced polymer (CFRP) strips with various configurations in terms of anchoring and tensioning. The five full-scale RC beams with the total length of 6.0 m were strengthened with passive strips, without and with mechanical anchorages at their ends, as well as with strips tensioned by the novel prestressing system with three various prestressing levels ranging from 30 to 50% of the CFRP tensile strength. All RC beams were tested under static flexural load up to failure and they were investigated in a full range of flexural behaviour, including the post-debonding phase. The main parameters considered in this study include the use of mechanical anchorages, the effect of tensioning the strips and the influence of the various prestressing levels. Several performance indicators have been established to evaluate the beams’ behaviour. The study revealed that the RC beams strengthened using tensioned CFRP strips exhibited a higher cracking, yielding and ultimate moments as compared to the beams with passively bonded CFRP strips. Moreover, increasing the beams’ prestressing level has a significant positive influence on the performance of strengthened beams. However, it did not affect the ultimate load-bearing capacity of the beams. The optimal prestressing level for the novel system has been determined as 60% of CFRP tensile strength.

Efficiency of Hybrid Algorithms for Estimating the Shear Strength of Deep Reinforced Concrete Beams

Earthquakes occurred in recent years have highlighted the need to examine the strength of reinforced concrete (RC) members. RC beams are one of the elements of reinforced concrete structures. Due to the dramatic increase in the population and the number of medium/high-rise buildings, in recent years, the beams of buildings have been mainly designed and executed in the type of deep beams. In this study, the artificial neural network (ANN) with optimization algorithms, including particle swarm optimization (PSO), Archimedes optimization algorithm (AOA), and sparrow search algorithm (SSA), are used to determine the shear strength of reinforced concrete deep (RCD) beams. 271 samples from experimental tests are employed to develop algorithms. The results of this study, design codes equations, and previous research are compared. Comparison between the results shows that the PSO-ANN algorithm is more accurate than previous methods. Finally, SHApley Additive exPlanations (SHAP) method is utilized to explain the predictions. SHAP reveals that the beam span and the ratio of the beam span to beam depth have the highest impact in predicting shear strength.