Low-code analysis of glass transition temperatures of structural strengthening adhesives

Structural strengthening adhesives are commonly used for bonding fibre-reinforced composites in the rehabilitation of civil engineering constructions. These adhesives are polymers whose performance is significantly influenced by their glass transition temperatures (<i>T</i>_<i>g</i>). A lower <i>T</i>_<i>g</i> can result in reduced stiffness and strength, particularly in warm temperature conditions. The <i>T</i>_<i>g</i> of adhesives is closely related to the types of adhesives and curing scenarios, yet predictions remain challenging. This study presents a low-code paradigm to examine the <i>T</i>_<i>g</i> values of strengthening adhesives. Multiple mainstream regression models were trained in parallel using 404 experimental data points and 606 synthetic data points generated by an advanced conditional tabular generative adversarial network. The final fine-tuned Light Gradient Boosting Machine (LightGBM) model demonstrated high accuracy in predicting <i>T</i>_<i>g</i> based on the curing conditions and type of adhesive, achieving a coefficient of determination of 0.890 for the test set. Furthermore, the low-code approach was employed to evaluate and interpret the performance of the LightGBM black-box model, and a corresponding web-based <i>T</i>_<i>g</i> prediction application was developed using a light-code platform called Streamlit, providing practical insights for engineers and researchers. The analysis of feature variations in the prediction results offers guidance for achieving optimal adhesive selection and curing strategies.