Umeå University's logo

Ultraviolet-cured glass fiber reinforced polymer (UV-GFRP) composites are widely used in cured-in-place pipe (CIPP) repair technology for buried pipelines. The bending strength is the key indicator for assessing repair quality, which is affected by multiple factors but lacks effective prediction methods yet. In this paper, a prediction model for the bending strength of UV-GFRP composites based on the archimedes optimization algorithm (AOA) combined with the extreme gradient boosting (XGBoost) algorithm is proposed, incorporating material structure design and curing parameters. Through hyperparameter optimization, robustness analysis, and sensitivity analysis, the model's performance and reliability are thoroughly evaluated. The results show that the AOA-XGBoost model achieves highly accurate prediction, with an R² of 0.906 on the test set, outperforming the backpropagation neural network optimized by genetic algorithm (GA-BPNN), support vector regression optimized by particle swarm optimization (PSO-SVR), random forest regression (RFR), gradient boosting decision tree (GBDT), and XGBoost. Notably, the model maintains stable predictions even under noise conditions of up to 10%. Sensitivity analysis reveals that fiber volume fraction (+0.338), glass fiber architecture (+0.205), and density (+0.178) have the most significant effect on the bending strength of UV-GFRP composites, which can be optimized to enhance material properties. Although curing parameters have a relatively smaller effect, careful adjustment is essential to prevent over-polymerization and degradation of material properties.