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.

Nanofluids are superior to pure fluids in heat transfer applications, such as direct solar collectors, due to enhanced thermophysical characteristics. Optimal dispersion is crucial, and direct ultrasonication is the most effective method in dispersing nanoparticles and preparing stable nanofluids; however, the optimal sonication time remains uncertain across different nanofluid types. In this study, alumina (Al₂O₃)–water nanofluid (0.1 wt%) and sodium dodecyl sulfate (SDS) surfactant were prepared using ultrasonication (up to 240 min). This study linked sonication duration to performance indicators—including zeta potential, particle size distribution, optical absorbance, pH, conductivity, and solar thermal conversion—to investigate dispersion and Photothermal Conversion Efficiency (PTE) under a solar simulator. The results indicate that sonication for up to 180 min enhances zeta potential, reduces agglomerate size …