| Abstract Scope |
Corrosion in marine environments poses a threat to the safety of engineering structures. The corrosion of steel bars induced by the penetration of chloride ions is the main reason for the deterioration of reinforced concrete structures. Novel fibre reinforced polymer (FRP) reinforced concrete structures are increasingly being developed and used in civil engineering to ensure their safety. The current American Concrete Institute (ACI) 440.1 R-15 guidelines for environmental reduction factors and long-term durability of GFRP (glass fibre reinforced polymers) rebars in harsh environments are based on limited experimental data and are deemed conservative. This study evaluated the GFRP rebars subjected to sustained loading under accelerated aging. Due to its eminent nature, tensile strength reduction of aged GFRP rebar was studied for durability evaluation using XGBoost model. Experimental data of 308 samples [1-5] were used in the investigation. The tensile strength reduction factor was studied as a function of the type of glass fibers, rebar surface, exposure type (bare / concrete embedded), the magnitude of sustained loading, type of resin, size of rebar, the volume fraction of fibers, pH of alkaline solution, temperature and duration of conditioning. The XGboost model was trained using the best hyperparameters obtained from grid tuning. The model displayed reliable results in terms of R-squared and mean absolute error for the training (0.98, 1.19 %) and test data (0.82, 3.87%), respectively. The developed model was used to study the behaviour of input variables towards tensile strength reduction using Shapely Additive explanations. The degradation of GFRP rebars is strongly influenced by key factors such as temperature, conditioning duration, solution pH, and the magnitude of sustained loading. It was observed that when the sustained load exceeded 20% of the ultimate tensile strength, the degradation process accelerated significantly. This finding is consistent with prior research, further validating the newly developed model in this study. The environmental reduction factor was determined using a machine learning approach combined with the Arrhenius relationship. Both methods were compared for accuracy. Additionally, a web-based application was developed to predict tensile strength degradation in alkaline environments. The findings and conclusions from this research contribute to a deeper understanding of the durability of GFRP rebars in challenging conditions. |