Enhanced creep strain prediction in diverse adhesively bonded joints using natural language processing-assisted transfer learning

Predicting creep strain is critical for ensuring the durability of adhesively bonded joints. Conventional machine learning methods can provide predictions based on available data, thus reducing the need for time-consuming physical tests. However, these methods often struggle with data scarcity and structural variability, particularly for specific types of joints. This study proposes a deep transfer learning (TL) framework designed for tabular data to address these challenges. The framework was developed using 482 experimental data points from bonded metallic joints for pre-training, while 88 data points from bonded polyethylene joints and 108 data points from bonded carbon fibre-reinforced polymer-steel joints were used for fine-tuning the final TL models. Natural language processing (NLP) concepts were adapted to the tabular creep datasets by treating input features with padding and masking to align heterogeneous feature spaces. These strategies enable the TL framework to transfer knowledge learned despite differences in available input variables. This enhances the robustness and generalisability of transfer learning for sparse, structurally variable tabular engineering data. Compared to the benchmark multilayer perceptron (MLP) models, both the TL and NLP-assisted TL models demonstrated superior performance. Leveraging precious learning experience from the large pre-training dataset, the TL model achieved coefficient of determination (R 2 ) values of 0.89 for the training set and 0.91 for the test set, whereas the MLP model yielded 0.83 (training) and 0.77 (test), indicating potential overfitting. Similar improvements were observed for the NLP-assisted TL model, which effectively managed input feature diversity across joint types. This research underscores the strengths and limitations of the proposed TL and NLP-assisted TL frameworks in overcoming tabular data scarcity and variability in engineering joint studies, offering valuable insights for safety-critical predictions.