Data-driven estimates of the strength and failure modes of CFRP-steel bonded joints by implementing the CTGAN method

Abstract

The bond strength between the CFRP and steel usually dominates the final strengthened effectiveness. However, the CFRP-steel bond strength is affected by various geometric and material properties and exhibits different failure modes, making accurate predictions challenging. This study utilises data-driven machine learning (ML) methods to predict the strength and failure modes of CFRP-steel joints. An experimental dataset consisting of 178 single-lap shear test results was first built, after which the Conditional Tabular Generative Adversarial Networks (CTGAN) method was applied to augment the limited available data. Four broadly used ML algorithms: Support Vector Machines (SVM), K-Nearest Neighbours (KNN), Decision Trees (DT) and Artificial Neural Networks (ANN) were applied. The ANN regression model achieved the best performance in predicting joint strength (Rtest2=0.95), while the SVM classification model achieved the best performance in predicting failure modes (accuracy ≥ 92.3 %). The SHapley Additive exPlanations analysis further revealed that the Young's modulus of the adhesive was most significant to the joint strength, while the tensile strength of the adhesive was most significant to the failure modes. The ultimately constructed ML models and the corresponding analyses presented can benefit practical structural engineering applications and provide insights into the optimal CFRP-steel joint design.