INTEGRATING EXPERIMENTAL ANALYSIS AND MACHINE LEARNING FOR ASSESSING BOND PERFORMANCE AND CORROSION SEVERITY IN REINFORCED CONCRETE STRUCTURES - Almashakbeh, Yousef (Journal of Applied Engineering Science, 2023)
This study presents a comprehensive investigation into the effects of corrosion on bond performance and mechanical properties of steel-concrete interfaces by combining experimental analysis with machine learning techniques. A total of 32 concrete prisms with varying water-cement (w/c) ratios were prepared and subjected to an accelerated corrosion process. Corrosion severity was assessed through visual inspection and weight loss measurements, while mechanical properties were evaluated through the pull-out tests. Experimental results showed that the residual bond load decreased to 77% and 81% for w/c ratios of 0.37 and 0.47, respectively, after accelerated corrosion. Additionally, corroded prisms exhibited significantly reduced residual toughness and stiffness compared to their non-corroded counterparts. To establish a correlation between bond slip and corrosion severity, a machine learning algorithm was developed and implemented. The algorithm achieved an accuracy of 100% for both studied w/c ratios and remarkably low costs after optimization (4.548 for w/c ratio of 0.37 and 3.445for w/c ratio of 0.47). This integrated approach provides valuable insights for future infrastructure assessment and maintenance efforts. In conclusion, this study combines laboratory findings with real-world applications. It provides a thorough understanding of the relationship between corrosion and bond performance in reinforced concrete structures. Maintaining the structural integrity and safety of reinforced concrete structures in corrosive environments can be aided by the results of this research.)