Machine Learning-Based Modeling of Tensile Properties of Glass-Fiber-Reinforced Polymer Pipes under Accelerated Saltwater Aging Conditions

Glass-fiber-reinforced polymer (GFRP) pipes are increasingly used in aggressive environments due to their high corrosion resistance and favorable mechanical properties. However, long-term exposure to saline environments and elevated temperatures can lead to degradation of their structural performance. This study investigates the influence of accelerated saltwater aging on the tensile behavior and structural characteristics of GFRP pipes and proposes machine-learning-based predictive models for the ultimate tensile strength (UTS). Experimental specimens were immersed in a 3.5% NaCl solution under controlled temperature and exposure time conditions. Tensile testing revealed that the unexposed samples exhibited a maximum UTS of 79.63 MPa, while aged specimens showed a gradual reduction in strength, although more than 80% of the initial tensile strength was retained after 60 days of exposure. Statistical analysis indicated that temperature was the dominant factor, contributing 60.24% to the variation in UTS, followed by exposure time with 33.72%, with the regression model explaining 93.96% of the total variance (R² = 0.9396). X-ray diffraction analysis revealed a decrease in the degree of crystallinity from 20.49% in the reference sample to 15.05% in the most degraded specimen, corresponding to an approximate 26.5% reduction, which correlated with the observed decline in mechanical strength. Several machine learning approaches were evaluated, including Artificial Neural Networks (ANN), Exponential Gaussian Process Regression, and Boosted Trees. Among them, ANN provided the highest predictive accuracy, demonstrating strong agreement between predicted and experimental UTS values. The results confirm that hydrothermal aging significantly affects both the microstructural and mechanical properties of GFRP pipes, while machine learning models represent effective tools for predicting their long-term performance under aggressive environmental conditions.