Carbon fiber reinforced polymers (CFRP) have shown considerable potential in the repair and rehabilitation of deficient reinforced concrete (RC) structures. To date, several CFRP strengthening schemes have been studied and employed practically. In particular, strengthening of shear damaged RC members with CFRP materials has received much attention as an effective repair and strengthening approach. Most existing studies on strengthening shear-deficient RC members have used unidirectional CFRP strips. Recent studies on strengthened T-beams demonstrated that a bidirectional CFRP layout was more effective than a unidirectional layout. As such studies are limited, in this study, the feasibility of bidirectional CFRP layouts for the shear strengthening of rectangular RC beams was experimentally evaluated. Bidirectional layout details with CFRP anchors as well as rehabilitation timing were considered and investigated. The test results showed that the members with a bidirectional CFRP layout carried less shear strength capacity than those with unidirectional layouts for the same quantity of CFRP material. Nevertheless, the bidirectional CFRP layout allowed for a uniformly distributed stirrup strain compared to the unidirectional CFRP layout at the same load level, which increased the efficiency of the transverse reinforcement. Additionally, the shear contribution of CFRP material according to the CFRP strengthening timing was verified.
Artificial neural networks (ANNs) are an emerging field of research and have proven to have significant potential for use in structural engineering. In previous literature, many studies successfully utilized ANNs to analyze the structures under different loading conditions and verified the accuracy of the approach. Several studies investigated the use of ANNs to analyze the shear behavior of reinforced concrete (RC) members. However, few studies have focused on the potential use of an ANN for analysis of the torsional behavior of an RC member. Torsion is a complex problem and modeling the torsional fracture mechanism using the traditional analytical approach is problematic. Recent studies show that the nonlinear behavior of RC members under torsion can be modeled using ANNs. This paper presents a comprehensive analytical and parametric study of the torsional response of RC beams using ANNs. The ANN model was trained and validated against an experimental database of 159 RC beams reported in the literature. The results were compared with the predictions of design codes. The results show that ANNs can effectively model the torsional behavior of RC beams. The parametric study presented in this paper provides greater insight into the torsional resistance mechanism of RC beams and its characteristic parameters.
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