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.

An innovative passive energy damper is introduced and studied experimentally and numerically. This damper is designed as the main plate for energy absorption which is surrounded by an octagon cover. In addition to simplicity in construction, it can be easily replaced after a severe earthquake. Experimental test results, as well as finite element results, indicated that, by connecting the cross-flexural plate to the main plate, the mechanism of the plate was changed from flexural to shear. However, the cross_flexural plate always acts as a flexural mechanism. Changing the shear mechanism to a flexural mechanism, on the other hand, increased the stiffness and strength, while it reduced the ultimate displacement. Comparing the hysteresis curve of specimens revealed that models without cross_flexural plates had less strength and energy_dissipating capability than other models. Adding the flexural plate to the damper without connecting to the main plate improved the behavior of the damper, mainly by improving the ultimate displacement. Connecting the cross plate to the web plate enhanced the ultimate strength and stiffness by 84% and 3.9, respectively, but it reduced the ductility by 2.25. Furthermore, relationships were proposed to predict the behavior of the dampers with high accuracy.

The reinforced concrete (RC) member’s shear strength estimation has been experimentally studied in most cases due to its nonlinear behavior. Many empirical equations have been derived from the experimental data; however, even those adopted in the construction codes do not thoroughly and accurately describe their shear behavior. Theoretically explained equations, on the other hand, are aligned with the experiment; however, they are complicated to use in practice. As shear behavior research is data-driven, the machine learning technique is applicable. Herein, an artificial neural network (ANN) algorithm is trained with 776 experiment results collected from available publications. The raw data is preprocessed by principal component analysis (PCA) before the application of the ANN technique. The predictions of the trained algorithm using ANN with PCA are compared to those of formulae adopted in a few existing building codes. Finally, a parametric study is conducted, and the significance of each variable to the strength of RC members is analyzed.

Beam–column joints are generally recognised as critical regions in the analysis and design of moment-resisting reinforced-concrete (RC) frames subjected to both lateral and vertical loads. Such joints should be designed and detailed to ensure that they have adequate overstrength and allow adjacent beams to reach their full ductile capacities. To develop an improved method for predicting the deformation capacity of RC beam–column joint assemblies, the deformation contributions of structural members connecting the joint should be considered. A method is proposed to predict the deformation component ratios of RC beam–column joint assemblies that fail in shear after the adjacent beams have reached their full plastic capacities. The proposed method takes into account the effect of the longitudinal axial strain in the plastic hinge region of beams on the joint longitudinal strain and the strength deterioration of the joint. To verify the deformations of typical RC beam–column joint assemblies estimated using the proposed method, six specimens were tested. Furthermore, 35 experimental results of joints reported in the technical literature were compared with the deformations predicted by the proposed method. Comparisons between the observed and calculated deformations of the considered RC beam–column joint assemblies showed reasonable agreement.

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.

In this study, a simplified cross-sectional approach based on finite-element analysis was developed to evaluate the flexural strengthening performance of fire-damaged RC beams using externally bonded steel plates and CFRP strips. The strength degradation of the simplified concrete cross section and reinforcing bars was determined based on the temperature field calculation results from FE software. The strength degradation models proposed in previous literature were adopted and applied based on the obtained temperature distribution. The finite-element model was validated with the previous experimental test to evaluate the accuracy of the model in predicting the residual flexural capacity of RC beams after fire exposure. It is shown that the finite-element model (FEM) was able to predict the flexural behavior of fire-damaged RC members reasonably well. The ISO-834 standard fire curve was applied to the reference flexure beam for 60 min, 90 min, and 120 min of heating before strengthening by employing the proposed methodology. The parametric study in this investigation includes the thickness and width of the strengthening materials for the comparison of the flexural capacity recovery of both retrofitting methods. The analysis results showed that the ultimate load and stiffness of fire-damaged beams strengthened with both materials improved significantly compared to the heated beams without strengthening. The damaged beams group retrofitted with externally bonded steel plates exhibited a greater increase in both ultimate load and stiffness compared to the damaged beams group retrofitted with externally bonded CFRP strips.