At present, global waste tire generation considerably exceeds consumption. Moreover, waste rubber tires (WRTs) are a cause of concern, as huge volumes are being discarded and buried, thus causing serious environmental pollution. Rubberized waste concrete (RWC) is a type of environmentally friendly construction material. The main challenge encountered when manufacturing rubberized concrete is the low adhesive properties between the cement paste and rubber particles. This paper demonstrates the effects, through experiments, of using waste tire rubber instead of recycled coarse aggregate (RCA) on two types of cement, i.e., sulfate-resistant cement (SRC) and ordinary Portland cement (OPC), where SRC is a specially blended cement designed to improve concrete performance and workability in the most aggressive environments. All tested samples contained 10% silica fume (SF) and 0.2% fly ash (FA), and the substitution of recycled aggregate content with waste rubber tier (WRT) at different percentages of 100%, 75%, and 50% was evaluated. The research investigated the synergistic effect on the workability and mechanical properties of various cement types with different amounts of rubber aggregate. It was found that the sulfate-resistant (SRC) type can increase the compressive strength than OPC with a percentage of 25% with the same content of WRT at concrete mix. Moreover, ductility and cracking behavior are improved, and it appears that it is also possible to make lightweight rubber aggregate concrete with this type of mixture.. Using this type of cement, it is possible to restore satisfactory ductility to the waste tires, thus facilitating a reduction in the formation of potential plastic cracks. Moreover, the indicative compressive strength development for SRC with recycled rubber in concrete positively contributes to a reduction in formed cracks. However, SEM microstructural analyses suggest a higher proportion of C–S–H intermixed with sulfate reaction phases of SRC rubberized mortar than those of OPC; thus, given that crystal growth results in a decreased percentage of air voids rather than decreased internal cracking, it is clearly shown that the average crack width increases in OPC mortar compared with SRC. Finally, t-testing was used as an inferential statistical tool to determine whether there is a sizeable distinction between the properties of the two categories of materials, OPC and SRC, by comparing the mean and standard deviation of the values for compressive and tensile strength.

Carbon Fiber Reinforced Polymer (CFRP) composites are widely used in reinforced concrete beams (RCBs) due to their high strength-to-weight ratio, corrosion resistance, and ease of application. However, accurately predicting the failure mode characteristics in these beams remains a significant challenge. This study presents a comprehensive framework for analyzing and predicting the failure modes of CFRP-strengthened RCBs using Deep Residual Neural Networks (DRNNs), finite element modeling (FEM), and Density-Based Sensitivity Analysis (DBSA). The proposed framework integrates experimental data and FEM simulations, leveraging DRNNs to model complex, nonlinear relationships between input parameters and failure mechanisms. DRNNs' architecture—comprising convolutional layers, residual building units, and batch normalization—facilitates accurate prediction of debonding loads, displacements, and failure modes. Residual shortcuts (i.e., connections), unlike other neural network architectures, allowed to bypass a few layers in the deep network architecture, circumventing the regular training with high accuracy problems. The DRNNs were instrumental in reducing reliance on large-scale physical experiments by generating synthetic data points required for DBSA convergence, making this framework practical for real-world applications. The study highlights the efficiency of DRNNs in reducing experimental requirements, achieving a high prediction accuracy of 90% and an AUC of 0.87. Validation of FEM with experimental results confirmed the framework's reliability in replicating real-world structural behavior. DBSA identified CFRP thickness and concrete density as critical factors influencing debonding load and displacement, while dry fiber density predominantly affects failure modes. Also, elastic properties of CFRP and concrete are essential for influencing debonding patterns.

This paper describes a theoretical study of forty eight (48) Reinforced Concrete RC beam column joints, which generally classified as interior, exterior and corner joints. The most main parameters, which may influence the behavior of beam-column joint, transverse reinforcement, lateral shear reinforcement, longitudinal reinforcement, joint area, column axial load, concrete compressive strength, end boundary conditions …etc. In this research FEA (finite element analysis) using ABAQUSCAE commercial software will be developed to build a detailed model to predict the nonlinear behavior of beam-column joint under seismic loadings. The ABAQUSCAE 6.7 solution is a practical way to implement the nonlinear dynamic earthquake analysis for the finite element model. Our study is focusing with joints where statically lateral load was applied and gradually increased maintaining constant axial load at the top of the column equal to 0.15 (AcFc). Several linear variable horizontal displacement transducers were mounted , the net story drift, bond stress, axial shear stresses and strains as well as energy absorption for using different grades of concrete C250, C400, C600 and C1200 are evaluated and discussed to illustrate the behaviour of the studied joints under the case study of loadings.