XGBoost and genetic programming methods for analysing the vitreous transition of the epoxy adhesive

Fibre-reinforced polymer (FRP) composites are increasingly favoured for strengthening existing structures due to their numerous structural benefits. Nevertheless, the performance of such technology is strongly affected by the behaviour of the epoxy resin adhesive layer, which is largely dependent on its curing conditions. This study introduces a deep learning (DL) framework that leverages eXtreme Gradient Boosting (XGBoost) and genetic programming (GP) to comprehensively study the influence of curing scenarios on the vitreous transition of the adhesive. An experimental dataset comprising 160 data points was used to develop predictive models. The XGBoost models exhibited high predictive accuracy for both the onset vitreous transition temperature and the peak tan δ vitreous transition temperature, achieving R2 values of 0.982 and 0.993 for the unseen test set, respectively. While the GP models exhibited lower predictive accuracy with R2 values of 0.834 and 0.842, they provided explicit equations that enhance the interpretability of the DL model and facilitate practical application. To make these insights accessible to engineers without programming expertise, a web-based graphical user interface software was developed, incorporating all DL models. Additionally, a feature influence assessment was conducted, providing visual representations of the impact of each feature on the output results, thus enhancing interpretability for engineering applications.