Evaluating the effect of curing conditions on the glass transition of the structural adhesive using conditional tabular generative adversarial networks

Songbo Wang*, Haixin Yang, Tim Stratford, Jiayi He, Biao Li, Jun Su

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Owing to its structural advantages, adhesively bonding fibre-reinforced polymers have been a promising solution for strengthening constructions. However, the effectiveness of this technique is significantly influenced by the material properties of the adhesive layer, which are largely determined by its curing condition. A comprehensive analysis of the effect of curing conditions on structural adhesives is hampered by the lack of sufficient experimental data. To mitigate such a limitation, this present study utilises a deep machine learning (ML) tool, the conditional tabular generative adversarial networks (CTGAN), to generate plausible synthetic dataset for developing a robust data-driven model. An artificial neural network (ANN) was trained on synthetic data and tested on real data, following the "Train on Synthetic – Test on Real" philosophy. The ultimately developed CTGAN-ANN model was validated by newly conducted experiments and several published studies (R2 ≥ 0.95), which demonstrated the ability to provide accurate estimates of the glass transition temperature values of the polymer adhesive. A comprehensive evaluation of the effect of each curing condition variable on the adhesive was performed, which revealed the underlying relationships, indicating that curing temperature and curing time have a positive effect, but that curing humidity has a negative effect. The ML model developed could inform the practical use of the structural adhesive in civil engineering.
Original languageEnglish
Article number107796
Number of pages12
JournalEngineering Applications of Artificial Intelligence
Volume130
Early online date30 Dec 2023
DOIs
Publication statusPublished - 1 Apr 2024

Fingerprint

Dive into the research topics of 'Evaluating the effect of curing conditions on the glass transition of the structural adhesive using conditional tabular generative adversarial networks'. Together they form a unique fingerprint.

Cite this