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Carbonfiber-reinforced polymer (CFRP) composites are frequently utilized in the repair and reinforcement of reinforced concrete members because they are more corrosion-resistant, durable, and flexible than commonly used materials like steel plates. Many studies have demonstrated that the use of CFRP as an internal strengthening material has a substantial impact on the shear capacity of RC beams. This research looks into the possibility of using FRP material as an externally bonded reinforcement to increase the shear strength of RC beams. Because shear improvement and mode of failure are based on the right configuration of the CFRP sheets, the experimental work consisted of eight RC beams with various types, orientations, and configurations of CFRP sheets. All beams were examined under two-point bending. The test results show that the CFRP sheet significantly improved the beam’s shear strength and ductility and that it is advantageous to orient the FRP at 45 degrees to the beam’s longitudinal axis.

Traditional Pavement Condition Index (PCI) assessments are highly resource-intensive, demanding substantial time and labor while generating significant carbon emissions through extensive field operations. To address these sustainability challenges, this research presents an innovative methodology utilizing Gene Expression Programming (GEP) to determine PCI values based on International Roughness Index (IRI) measurements from Iraqi road networks, offering an environmentally conscious and resource-efficient approach to pavement management. The study incorporated 401 samples of IRI and PCI data through comprehensive visual inspection procedures. The developed GEP model exhibited exceptional predictive performance, with coefficient of determination (R2) values achieving 0.821 for training, 0.858 for validation, and 0.8233 overall, successfully accounting for approximately 82–85% of PCI variance. Prediction accuracy remained robust with Mean Absolute Error (MAE) values of 12–13 units and Root Mean Square Error (RMSE) values of 11.209 and 11.00 for training and validation sets, respectively. The lower validation RMSE suggests effective generalization without overfitting. Strong correlations between predicted and measured values exceeded 0.90, with acceptable relative absolute error values ranging from 0.403 to 0.387, confirming model effectiveness. Comparative analysis reveals GEP outperforms alternative regression methods in generalization capacity, particularly in real-world applications. This sustainable methodology represents a cost-effective alternative to conventional PCI evaluation, significantly reducing environmental impact through decreased field operations, lower fuel consumption, and minimized traffic disruption. By streamlining pavement management while maintaining assessment reliability and accuracy, this approach supports environmentally responsible transportation systems and aligns contemporary sustainability goals in infrastructure management.