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This study investigates the corrosion behavior of 5 mm diameter prestressed wires in concrete beams under chloride attack, a prevalent issue for coastal infrastructure. The study simulated aggressive chloride environments to understand their impact on structural integrity and service life. Utilizing a combination of advanced digital image correlation (DIC) techniques and a novel machine learning-based predictive model, the research provides a nuanced analysis of the interplay between stress levels, corrosion rates, and concrete strength. Empirical findings reveal a significant correlation between increased prestress levels and accelerated corrosion, indicating a crucial consideration for the design and maintenance of prestressed concrete structures. Notably, this study found that beams with a 95% prestress level exhibited a corrosion rate of 0.64 mm/year, significantly higher than the 0.37 mm/year for non-prestressed beams. The predictive model’s accuracy was validated with a mean squared error of 0.517 and an R2 value of 0.905, offering a valuable tool for quantifying the impact of corrosion. Therefore, the predictive model is a valuable tool for quantifying the impact of corrosion, enhancing the ability to assess and improve the durability of such infrastructure. This study’s insights highlight the necessity for a balanced approach to design and regular monitoring, especially in chloride-rich environments. By helping to develop more resilient construction practices and contributing to sustainable development goals, this study can significantly impact the safety and service life of coastal bridges and structures, aligning with global efforts to create more sustainable and durable infrastructure.
Pierott et al. (Sat,) studied this question.